JP2020518311A - System for developing one or more patient-specific spinal implants - Google Patents

Abstract

The disclosure herein relates to systems, methods and devices for developing patient-specific spinal therapies, surgeries, and procedures. In some embodiments, the systems, methods, and devices described herein for the development of patient-specific spinal therapy, surgery and procedures can include repetitive virtuous cycles. Repeated virtuous circulation may further include pre-, intra-, and post-operative techniques or steps. For example, the iterative virtuous cycle can include image analysis, case simulation, implant making, case assistance, data collection, machine learning, and/or predictive modeling. One or more techniques or steps of repeated virtuous cycles can be repeated. [Selection diagram]

Description

This application relates to systems for developing one or more patient-specific spinal implants. The present application also relates to spinal rods and surgical planning and procedures.

Spinal surgery is one of the most commonly performed surgical procedures in the world. Generally speaking, spinal surgery involves the implantation of spinal rods to correct the curvature of the patient's spine and prevent further deterioration. Therefore, the individual curvature of the spinal rod is an important factor for successful surgery.

The various embodiments described herein relate to systems, methods, and devices for developing patient-specific spinal treatments, surgeries, and procedures. In some embodiments, the systems, methods, and devices described herein for the development of patient-specific spinal therapy, surgery and procedures can include repetitive virtuous cycles. Repeated virtuous circles may further include pre-, intra-, and post-operative techniques or steps. For example, an iterative virtuous cycle can include image analysis, case simulation, implant making, case assistance, data collection, machine learning, and/or predictive modeling. One or more techniques or steps of repeated virtuous cycles can be iterative.

The present invention relates to a system for developing one or more patient-specific spinal implants, the system comprising:
One or more computer-readable storage devices configured to store multiple computer-executable instructions; and communicating with one or more computer-readable storage devices to execute multiple computer-executable instructions The system
Accessing one or more medical images of the patient's spine;
Based on one or more medical images,
Identifying one or more reference points along the spinal segment of interest; and rotating one or more portions of the one or more medical images around the identified one or more reference points To obtain the desired surgical output curvature of the patient's spine,
Simulating the implantation of a spinal rod into the spinal segment of interest, including:
Determining one or more dimensions, including diameter and curvature, of a patient-specific spinal rod for a spinal segment of interest, based at least in part on a spinal rod implantation simulation;
A spinal rod manufacturing or selecting apparatus for making or selecting a patient-specific spinal rod for a spinal segment of interest based at least in part on the determined size of implantation of one or more patient-specific spinal rods. Generating spinal rod manufacturing or data selection instructions for use;
Determining the anterior longitudinal ligament length of the spinal segment of interest and the posterior longitudinal ligament length of the spinal segment of interest from one or more medical images;
Determining the anterior curve length of the spinal segment of interest and the posterior curve length of the spinal segment of interest from one or more medical images;
Based on one or more medical images,
Increasing the posterior height of each of the one or more cages to a length in which the posterior curve is substantially equal to or less than the length of the posterior longitudinal ligament; and shorter than the anterior longitudinal ligament Increasing the lordosis of each of one or more cages while maintaining the anterior curve length,
Simulating implantation of one or more cages into one or more intervertebral spaces within the spinal segment of interest further comprising:
One or more including posterior and anterior heights of each of the one or more patient-specific cages for the spinal segment of interest, based at least in part on a simulation of implantation of one or more cages. And one or more for one or more intervertebral space based at least in part on the determined one or more dimensions of each of the one or more patient-specific cages. Generating a cage manufacturing or data selection instruction for use with a cage manufacturing or selecting apparatus for manufacturing or selecting a patient-specific cage of
Including one or more hardware computer processors configured to perform
A patient-specific spinal rod for a spinal segment of interest is made or selected from an existing range of spinal rods based, at least in part, on the generated spinal rod manufacturing or data selection instructions. One or more patient-specific cages for use are made or selected from an existing range of cages based at least in part on the generated cage manufacturing or data selection instructions.

In one embodiment, the system is
Determining the screw insertion axis projection length on the sagittal plane and the vertebral body width for each vertebra in the spinal segment of interest from one or more medical images;
Based on the given spinal anatomical data, literature, or surgeon preference, for each vertebra in the spinal segment of interest, based on at least in part, the implantation of a screw based on an end plate configured to attach the screw. Assumed or prescribed angle formation, assumed or prescribed angle between vertebral axis and pedicle axis on cross section, assumed or prescribed ratio between screw length and screw insertion axis length, and vertebral body width and pedicle Determining an assumed or predetermined ratio between the width;
The determined screw insertion axis projected length on the sagittal plane, the determined vertebral body width, the assumed or predetermined angle between the vertebral axis and the pedicle axis on the cross section, and the screw length and the screw insertion axis length Generate one or more desired lengths of one or more patient-specific screws for insertion into each vertebra in the spinal segment of interest based at least in part on an assumed or predetermined ratio between To do;
A spinal segment of interest based at least in part on the determined screw insertion axial projection length on the sagittal plane, the determined vertebral body width, and an assumed or predetermined ratio between vertebral body width and pedicle width. Producing one or more desired diameters of one or more patient-specific screws for insertion into each vertebra therein; and at least part of the determined one or more desired lengths and diameters Generating a screw manufacturing or data selection instruction for use by a screw manufacturing or selecting device for making or selecting one or more patient-specific screws from an existing range of screws based on: Is further implemented.

In one embodiment, at least one of the one or more patient-specific screws comprises one or more sensors, wherein the one or more sensors are configured to obtain intraoperative follow-up data and The tracking data includes substantially real-time orientation and position data of a portion of the patient's spine and the intraoperative tracking data is configured to assist the surgical procedure.

In one embodiment, the one or more patient-specific screws are configured to be inserted into one or more vertebrae with a surgical tool, the surgical tool including one or more sensors, The one or more sensors are configured to obtain intraoperative tracking data, the intraoperative tracking data including substantially real-time orientation and position data of a portion of a patient's spine, the intraoperative tracking data indicating a surgical procedure. Configured to assist.

In one embodiment, the desired surgical output curvature of the spine is determined based at least in part on one or more predicted post-operative parameters,
the system,
UIL, LIL, patient age, pelvic morphology preoperative value, pelvic tilt preoperative value, lumbar lordotic preoperative value, thoracic kyphosis preoperative value, or sagittal plane by analyzing one or more medical images Determining one or more preoperative variables associated with the patient's spine, including vertical axis preoperative values; and predicting one or more postoperative variables based at least in part on the application of the predictive model Generating a value, where the prediction model is
Accessing a data set of an electronic database containing data collected from one or more previous patients and spinal surgery strategies adopted by the one or more previous patients;
Grouping the dataset into one or more categories based on spinal surgery area knowledge;
Normalizing the data to the first subcategory;
Selecting a model algorithm for the data in the first subcategory;
Inputting a first set of input values from the first subcategory into a model algorithm and training a predictive model based on the first set of output values from the first subcategory;
Inputting a second set of input values from the second subcategory into the trained prediction model and comparing the results produced by the trained prediction model with the second set of output values from the second subcategory; And storing the trained predictive model for implementation,
Generate a predicted value, generated by
Further generating predictive values for one or more post-operative parameters by
Post-operative parameters include one or more of pelvic tilt, lumbar lordosis, thoracic kyphosis, or sagittal vertical axis.

In one embodiment, the one or more medical images of the spine include one or more of sagittal, frontal, flexion, stretched, or MRI images. ..

In one embodiment, the one or more medical images include one or more two-dimensional X-ray images and the system calibrates the one or more two-dimensional X-ray images. Generating a three-dimensional composite image based on the three-dimensional X-ray image is further performed.

The invention also relates to a system for developing one or more patient-specific spinal implants, the system comprising:
One or more computer-readable storage devices configured to store computer-executable instructions; and communicating with one or more computer-readable storage devices to execute computer-executable instructions The system
Accessing one or more medical images of the patient's spine;
Determining the anterior longitudinal ligament length of the spinal segment of interest and the posterior longitudinal ligament length of the spinal segment of interest from one or more medical images;
Determining the anterior curve length of the spinal segment of interest and the posterior curve length of the spinal segment of interest from one or more medical images;
Based on one or more medical images,
Increasing the posterior height of each of the one or more cages to a length in which the posterior curve is substantially equal to or less than the length of the posterior longitudinal ligament; and shorter than the anterior longitudinal ligament Increasing the lordosis of each of one or more cages while maintaining the anterior curve length,
Simulating implantation of one or more cages into one or more intervertebral spaces within the spinal segment of interest further comprising:
One or more including posterior and anterior heights of each of the one or more patient-specific cages for the spinal segment of interest, based at least in part on a simulation of implantation of one or more cages. Determining the dimensions of;
An existing range of cages based at least in part on the cage manufacturing or data selection instructions generated based at least in part on each of the determined one or more dimensions of each of the one or more patient-specific cages. Manufacturing or data selection instructions for use by a cage manufacturing or selecting apparatus for manufacturing or selecting one or more patient-specific cages for one or more intervertebral spaces made or selected from To generate,
Determining the screw insertion axis projection length on the sagittal plane and the vertebral body width for each vertebra in the spinal segment of interest from one or more medical images;
Based on the given spinal anatomical data, literature, or surgeon preference, for each vertebra in the spinal segment of interest, based on at least in part, the implantation of a screw based on an end plate configured to attach the screw. Assumed or prescribed angle formation, assumed or prescribed angle between vertebral axis and pedicle axis on cross section, assumed or prescribed ratio between screw length and screw insertion axis length, and vertebral body width and pedicle Determining an assumed or predetermined ratio between the width;
The determined screw insertion axis projected length on the sagittal plane, the determined vertebral body width, the assumed or predetermined angle between the vertebral axis and the pedicle axis on the cross section, and the screw length and the screw insertion axis length Generate one or more desired lengths of one or more patient-specific screws for insertion into each vertebra in the spinal segment of interest based at least in part on an assumed or predetermined ratio between To do;
A spinal segment of interest based at least in part on the determined screw insertion axial projection length on the sagittal plane, the determined vertebral body width, and an assumed or predetermined ratio between vertebral body width and pedicle width. Producing one or more desired diameters of one or more patient-specific screws for insertion into each vertebra therein; and at least part of the determined one or more desired lengths and diameters Generating a screw manufacturing or data selection instruction for use by a screw manufacturing or selecting device for making or selecting one or more patient-specific screws from an existing range of screws based on:
And one or more hardware computer processors configured to perform.

In one embodiment, at least one of the one or more patient-specific screws comprises one or more sensors, wherein the one or more sensors are configured to obtain intraoperative follow-up data and The tracking data includes substantially real-time orientation and position data of a portion of the patient's spine and the intraoperative tracking data is configured to assist the surgical procedure.

In one embodiment, the one or more patient-specific screws are configured to be inserted into one or more vertebrae with a surgical tool, the surgical tool including one or more sensors, The one or more sensors are configured to obtain intraoperative tracking data, the intraoperative tracking data including substantially real-time orientation and position data of a portion of a patient's spine, the intraoperative tracking data indicating a surgical procedure. Configured to assist.

1. In one embodiment, the system is
Based on one or more medical images,
Identifying one or more reference points along the spinal segment of interest; and rotating one or more portions of the one or more medical images around the identified one or more reference points To obtain the desired surgical output curvature of the patient's spine,
Simulating the implantation of a spinal rod in the spinal segment of interest, further comprising:
Determining one or more dimensions, including diameter and curvature, of a patient-specific spinal rod for a spinal segment of interest, based at least in part on a spinal rod implantation simulation;
Use in a spinal rod manufacturing or selection device for making or selecting a patient-specific spinal rod for a spinal segment of interest based at least in part on the determined dimensions of the one or more patient-specific spinal rods. Generating spinal rod manufacturing or data selection instructions for,
Is further implemented,
The patient-specific spinal rod for the spinal segment of interest is made or selected from an existing range of spinal rods based at least in part on the generated spinal rod manufacturing or data selection instructions.

2. In one embodiment, the desired surgical output curvature of the spine is determined based at least in part on one or more predicted post-operative parameters,
the system,
UIL, LIL, patient age, pelvic morphology preoperative value, pelvic tilt preoperative value, lumbar lordotic preoperative value, thoracic kyphosis preoperative value, or sagittal plane by analyzing one or more medical images Determining one or more preoperative variables associated with the patient's spine, including vertical axis preoperative values; and predicting one or more postoperative variables based at least in part on the application of the predictive model Generating a value, where the prediction model is
Accessing a data set of an electronic database containing data collected from one or more previous patients and spinal surgery strategies adopted by the one or more previous patients;
Grouping the dataset into one or more categories based on spinal surgery area knowledge;
Normalizing the data to the first subcategory;
Selecting a model algorithm for the data in the first subcategory;
Inputting a first set of input values from the first subcategory into a model algorithm and training a predictive model based on the first set of output values from the first subcategory;
Inputting a second set of input values from the second subcategory into the trained prediction model and comparing the results produced by the trained prediction model with the second set of output values from the second subcategory; And generating a predictor, generated by storing the trained prediction model for implementation,
Further generating predictive values for one or more post-operative parameters by
Post-operative parameters include one or more of pelvic tilt, lumbar lordosis, thoracic kyphosis, or sagittal vertical axis.

In one embodiment, the one or more medical images of the spine include one or more of sagittal, frontal, flexion, stretched, or MRI images. ..

In one embodiment, the one or more medical images include one or more two-dimensional X-ray images and the system calibrates the one or more two-dimensional X-ray images. Generating a three-dimensional composite image based on the three-dimensional X-ray image is further performed.

The invention also relates to a system for developing one or more patient-specific spinal implants, the system comprising:
One or more computer-readable storage devices configured to store computer-executable instructions; and communicating with one or more computer-readable storage devices to execute computer-executable instructions The system
Accessing one or more medical images of the patient's spine;
Determining the height of each of the one or more intervertebral discs of the spinal segment of interest from the one or more medical images;
For each of the one or more intervertebral discs of the spinal segment of interest, the disc height as a percentage of the total disc height of the spinal segment of interest and/or the disc angulation as a percentage of the total disc angulation of the spinal segment of interest. To make a decision;
The determined disc height percentage and/or the determined disc angulation percentage of each of the one or more intervertebral discs of the target spinal segment is determined by one or more corresponding disc populations of the asymptomatic population of intervertebral discs. Analyzing by comparing with a percentage and/or a predetermined percentage of disc angulation, wherein a predetermined disc height percentage and/or a predetermined disc angulation of the discs of one or more corresponding asymptomatic populations Analyzing, percentages being updated regularly and/or continuously;
Based on the one or more medical images, one or more corresponding discrepancies of the determined disc height percentage and/or the determined disc angulation percentage of each of the one or more discs of the spinal segment of interest. One or more in the spinal segment of interest of one or more cages based, at least in part, on a predetermined disc height percentage and/or comparison with a predetermined disc horning percentage of the discs of the symptomatic population. Simulating implantation in the intervertebral space;
Based at least in part on the simulation of implantation of one or more cages, the posterior and anterior height and/or angulation of each of the one or more patient-specific cages for the spinal segment of interest is determined. Determining one or more dimensions that include;
An existing range of cages based at least in part on the cage manufacturing or data selection instructions generated based at least in part on each of the determined one or more dimensions of each of the one or more patient-specific cages. Manufacturing or data selection instructions for use by a cage manufacturing or selecting apparatus for manufacturing or selecting one or more patient-specific cages for one or more intervertebral spaces made or selected from To generate,
Determining the screw insertion axis projection length on the sagittal plane and the vertebral body width for each vertebra in the spinal segment of interest from one or more medical images;
Based on the given spinal anatomical data, literature, or surgeon preference, for each vertebra in the spinal segment of interest, based on at least in part, the implantation of a screw based on an end plate configured to attach the screw. Assumed or prescribed angle formation, assumed or prescribed angle between vertebral axis and pedicle axis on cross-section, assumed or prescribed ratio between screw length and screw insertion axis length, and vertebral body width and pedicle Determining an assumed or predetermined ratio between the width;
The determined screw insertion axis projected length on the sagittal plane, the determined vertebral body width, the assumed or predetermined angle between the vertebral axis and the pedicle axis on the cross section, and the screw length and the screw insertion axis length. Generate one or more desired lengths of one or more patient-specific screws for insertion into each vertebra in the spinal segment of interest based at least in part on an assumed or predetermined ratio between To do;
Based on the determined screw insertion axial projection length on the sagittal plane, the determined vertebral body width, and an assumed or predetermined ratio between the vertebral body width and the pedicle width, at least in part, the spinal segment of interest. Producing one or more desired diameters of one or more patient-specific screws for insertion into each vertebra in; and at least part of the determined one or more desired lengths and diameters Generating a screw manufacturing or data selection instruction for use by a screw manufacturing or selection device for making or selecting one or more patient-specific screws from an existing range of screws based on:
And one or more hardware computer processors configured to perform.

In one embodiment, at least one of the one or more patient-specific screws comprises one or more sensors, wherein the one or more sensors are configured to obtain intraoperative follow-up data and The tracking data includes substantially real-time orientation and position data of a portion of the patient's spine and the intraoperative tracking data is configured to assist the surgical procedure.

In one embodiment, the system is
Based on one or more medical images,
Identifying one or more reference points along the spinal segment of interest; and rotating one or more portions of the one or more medical images around the identified one or more reference points To obtain the desired surgical output curvature of the patient's spine,
Simulating the implantation of a spinal rod in the spinal segment of interest, further comprising:
Determining one or more dimensions, including diameter and curvature, of a patient-specific spinal rod for a spinal segment of interest, based at least in part on a spinal rod implantation simulation;
Use in a spinal rod manufacturing or selection device for making or selecting a patient-specific spinal rod for a spinal segment of interest based at least in part on the determined dimensions of the one or more patient-specific spinal rods. Generating spinal rod manufacturing or data selection instructions for,
Is further implemented,
The patient-specific spinal rod for the spinal segment of interest is made or selected from an existing range of spinal rods based at least in part on the generated spinal rod manufacturing or data selection instructions.

3. In one embodiment, the desired surgical output curvature of the spine is determined based at least in part on one or more predicted post-operative parameters,
the system,
UIL, LIL, patient age, pelvic morphology preoperative value, pelvic tilt preoperative value, lumbar lordotic preoperative value, thoracic kyphosis preoperative value, or sagittal plane by analyzing one or more medical images Determining one or more preoperative variables associated with the patient's spine, including vertical axis preoperative values; and predicting one or more postoperative variables based at least in part on the application of the predictive model Generating a value, where the prediction model is
Accessing a data set of an electronic database containing data collected from one or more previous patients and spinal surgery strategies adopted by the one or more previous patients;
Grouping the dataset into one or more categories based on spinal surgery area knowledge;
Normalizing the data to the first subcategory;
Selecting a model algorithm for the data in the first subcategory;
Inputting a first set of input values from the first subcategory into a model algorithm and training a predictive model based on the first set of output values from the first subcategory;
Inputting a second set of input values from the second subcategory into the trained prediction model and comparing the results produced by the trained prediction model with the second set of output values from the second subcategory; And storing the trained predictive model for implementation,
Generate a predicted value, generated by
Further generating predictive values for one or more post-operative parameters by
Post-operative parameters include one or more of pelvic tilt, lumbar lordosis, thoracic kyphosis, or sagittal vertical axis.

In one embodiment, the one or more medical images of the spine include one or more of sagittal, frontal, flexion, stretched, or MRI images. ..

In one embodiment, the one or more medical images include one or more two-dimensional X-ray images and the system calibrates the one or more two-dimensional X-ray images. Generating a three-dimensional composite image based on the three-dimensional X-ray image is further performed.

For purposes of this summary, certain aspects, advantages, and novel features of the embodiments of the invention have been described herein. It is to be understood that all such advantages may not necessarily be achieved by any particular embodiment of the present invention. Thus, for example, a person of ordinary skill in the art would be able to achieve one or a group of advantages taught herein without necessarily having to achieve the other advantages taught or suggested herein. It will be appreciated that the present invention may be practiced or practiced.

All of these embodiments are intended to be within the scope of the invention disclosed herein. These and other embodiments will be readily apparent to those of ordinary skill in the art from the following detailed description, taken in conjunction with the accompanying drawings, and the invention is not limited to any particular disclosed embodiment. Absent.

The apparatus and methods described herein may be better understood by reference to the following description in conjunction with the accompanying drawings.

3 is a flow chart outlining an exemplary embodiment of a repeated virtuous cycle for developing patient-specific spinal therapy, surgery, and procedures. 3 is a flow chart illustrating an exemplary embodiment of pre-operative image analysis and/or case simulation for developing patient-specific spinal therapy, surgery, and procedures. 6 is a flow chart illustrating an exemplary embodiment of implant preparation, case support, and/or data collection for developing patient-specific spinal therapy, surgery, and procedures. 3 is a flow chart illustrating an exemplary embodiment of data collection, machine learning, and/or predictive modeling to develop patient-specific spinal therapy, surgery, and procedures. 1 illustrates an exemplary embodiment of case simulation delivery for developing patient-specific spinal therapy, surgery, and procedures. 1 illustrates an exemplary embodiment of case simulation delivery for developing patient-specific spinal therapy, surgery, and procedures. 1 illustrates an exemplary embodiment of a spinal rod that can be made and/or selected using certain embodiments of systems, devices, and methods for patient-specific spinal therapy, surgery, and procedures. 1 illustrates an exemplary embodiment of a spinal rod that can be made and/or selected using certain embodiments of systems, devices, and methods for patient-specific spinal therapy, surgery, and procedures. 1 illustrates an exemplary embodiment of a cage that can be made and/or selected using certain embodiments of systems, devices, and methods for patient-specific spinal therapy, surgery, and procedures. 1 illustrates an exemplary embodiment of a screw that can be made and/or selected using certain embodiments of systems, devices, and methods for patient-specific spinal therapy, surgery, and procedures. 9 is a flow chart illustrating an exemplary embodiment of cage and/or screw design, fabrication, modification, and/or selection. 9 is a flow chart illustrating an exemplary embodiment of cage design, fabrication, modification, and/or selection. 3 illustrates an exemplary embodiment of cage design, fabrication, modification, and/or selection. 3 illustrates an exemplary embodiment of cage design, fabrication, modification, and/or selection. 9 is a flow chart illustrating an exemplary embodiment of cage design, fabrication, modification, and/or selection. 3 illustrates an exemplary embodiment of cage design, fabrication, modification, and/or selection. 3 illustrates an exemplary embodiment of cage design, fabrication, modification, and/or selection. 9 is a flow chart illustrating an exemplary embodiment of cage design, fabrication, modification, and/or selection. FIG. 6 is a schematic diagram illustrating an exemplary embodiment of cage design, fabrication, modification, and/or selection. 6 is a flow chart illustrating an exemplary embodiment of screw design, fabrication, modification, and/or selection. FIG. 6 is a schematic diagram illustrating certain aspects of an exemplary embodiment of screw design, fabrication, modification, and/or selection. FIG. 3 is a schematic diagram illustrating an exemplary embodiment of intraoperative tracking. 3 illustrates an exemplary embodiment of a screw and/or sensor that can be used for intraoperative tracking. 3 illustrates an exemplary embodiment of a screw and/or sensor that can be used for intraoperative tracking. 3 illustrates an exemplary embodiment of a screw and/or sensor that can be used for intraoperative tracking. 3 illustrates an exemplary embodiment of a screw and/or sensor that can be used for intraoperative tracking.

3 illustrates an exemplary embodiment of a screw and/or sensor that can be used for intraoperative tracking.
3 illustrates an exemplary embodiment of a screw and/or sensor that can be used for intraoperative tracking. 1 illustrates an exemplary embodiment of tools and/or sensors that can be used for intraoperative tracking. 1 illustrates an exemplary embodiment of tools and/or sensors that can be used for intraoperative tracking. 1 illustrates an exemplary embodiment of tools and/or sensors that can be used for intraoperative tracking. 1 illustrates an exemplary embodiment of tools and/or sensors that can be used for intraoperative tracking. 1 illustrates an exemplary embodiment of tools and/or sensors that can be used for intraoperative tracking. 3 is a flow chart illustrating an exemplary embodiment of predictive modeling. 1 is a schematic diagram illustrating an embodiment of a system for developing patient-specific spinal therapy, surgery, and procedures. FIG. 1 is a block diagram illustrating an embodiment of a computer hardware system configured to execute software for implementing one or more embodiments of a system for developing patient-specific spinal therapy, surgery, and procedures. is there.

Although some embodiments, examples, and illustrations are disclosed below, those of ordinary skill in the art will appreciate that the invention described herein extends beyond the specifically disclosed embodiments, examples, and illustrations. It will be understood that it extends to and covers other uses and obvious modifications and equivalents of the invention. Embodiments of the present invention are described with reference to the accompanying drawings, wherein like numerals refer to like elements throughout. The terms used in the description provided herein are in some way limited or restricted for the simple reason that they are used in connection with the detailed description of the specific embodiments of the invention. Is not intended to be interpreted as. Furthermore, embodiments of the present invention include several novel features, no single feature is believed to be involved in its desirable attributes or essential to the practice of the invention described herein.

Spinal surgery is one of the most commonly performed surgical procedures in the world. Generally speaking, spinal surgery involves the implantation of one or more implants, such as spinal rods, cages and/or one or more screws, to correct the curvature of the patient's spine and prevent further deterioration. Can be accompanied. Therefore, the correspondence between one or more spinal implants and the patient's anatomy may be an important factor for successful surgery. In particular, the individual curvatures, dimensions, shapes and/or sizes of one or more spinal rods, cages and/or screws are very important for successful surgical results.

The various embodiments described herein relate to systems, methods, and devices for developing patient-specific spinal treatments, surgeries, and procedures. In some embodiments, the systems, methods, and devices described herein for the development of patient-specific spinal therapy, surgery and procedures can include repetitive virtuous cycles. Repeated virtuous circles may further include pre-, intra-, and post-operative techniques or steps. For example, an iterative virtuous cycle can include image analysis, case simulation, implant making, case assistance, data collection, machine learning, and/or predictive modeling. One or more techniques or steps of repeated virtuous cycles can be iterative.

In particular, the desired surgical outcome may be unique to each patient. For example, it should be understood from past data, experience, and/or literature that the spine of an individual patient should be corrected in a particular manner and/or to a certain extent based on the current condition of the patient's spine. I can know. Furthermore, specific dimensions and/or other variables for one or more implants specific to an individual patient may be designed, generated and/or otherwise constructed to obtain such orthodontic results. It may be convenient to do so. For example, there may be one or more desirable variables and/or parameters for one or more spinal rods, cages, and/or screws for implantation in a particular patient. Accordingly, the particular systems, devices, and methods described herein are constructed utilizing one or more medical images of a patient and analyzed to analyze one or more spinal rods for implantation. , Cage, and/or one or more desired parameters and/or variables of the screw are determined. Based on the determined one or more desired parameters and/or variables, certain systems, devices, and methods described herein may be customized to one or more individual patients. To produce, make, modify, select, provide, and/or generate instructions for selecting instructions to manufacture, make, modify, and/or select spinal rods, cages, and/or screws of May be further configured.

In addition to designing, making, and/or otherwise obtaining an ideal or desired spinal implant, it is more important that such implants be properly implanted according to the desired and/or prescribed surgical plan. If not, it can be just as important. In other words, even if one or more spinal rods, cages, and/or screws for a particular patient are made, selected, or otherwise obtained, implantation or other surgical procedures may be desired or predetermined If not followed, the effect can be limited. Therefore, it may be advantageous to ensure, or at least increase, the opportunity for surgery or its procedures to be performed as desired. Because of these effects, the particular systems, devices, and methods described herein provide intraoperative tracking that enables guidance and/or performance assessment during spinal surgery.

Moreover, although all patients are different and may have unique spinal conditions, some spinal conditions are more similar than others. Also, the individual characteristics of a particular patient's spinal condition may be more similar than others. Therefore, it may be advantageous to be able to analyze and utilize data regarding the spinal condition of a particular patient preoperatively and/or postoperatively in order to predict the outcome of spinal surgery in a new patient. Predictive analysis can also be used to generate a patient-specific surgical plan, which can include one or more parameters and/or variables for one or more spinal rods, cages, and/or screws. Accordingly, certain systems, methods, and devices disclosed herein are configured to utilize predictive modeling to generate predictive surgical outcomes and/or patient-specific surgical plans.

Iterative Virtuous Circuit FIG. 1 is a flow chart outlining an exemplary embodiment of an iterative virtuous cycle for developing patient-specific spinal therapy, surgery, and procedures. As illustrated in FIG. 1, some embodiments of systems, methods, and devices include one or more steps that can form a recurrent virtuous cycle. For example, a repetitive virtuous cycle can include one or more of the following: (1) image analysis; (2) case simulation; (3) implant creation; (4) case support; (5) data collection; (6) machine learning; and/or (7) predictive modeling. Embodiments may include any subset of the steps described above. Moreover, one or more steps or techniques of repeated virtuous cycles can be repeated.

A particular step or technique of repeated virtuous cycle can be performed at different times. For example, image analysis, case simulation, and/or implant preparation can be performed pre-operatively. Case support and/or data collection may be performed intraoperatively or intraoperatively. Finally, some data collection, machine learning, and/or predictive modeling can be performed post-operatively. In certain embodiments, the entire repetitive virtuous cycle and/or portions thereof can be repeated for the same patient and/or different patients.

FIG. 2 is a flow chart illustrating an exemplary embodiment of pre-operative image analysis and case simulation for developing patient-specific spinal therapy, surgery, and procedures. Image analysis can be associated with analysis of one or more medical images. For example, one or more frontal and sagittal X-ray images of the patient may be analyzed. In other embodiments, the one or more medical images may include MRI scans, other x-ray images, CT scans, and/or any other medical image. Analyzing the one or more medical images may include rendering the one or more rod designs as an overlay on the one or more medical images. For example, in some embodiments, one or more rods with individual curvatures can be rendered as an overlay on one or more radiographic images.

As shown in FIG. 2, in some embodiments, the medical imaging system can be configured to obtain and/or access one or more medical images of the patient at block 202. The one or more medical images can be two-dimensional and/or three-dimensional. The one or more medical images can be sagittal x-rays, frontal x-rays, flexion x-rays, stretch x-rays, CT scans, and/or MRI scans of the patient's spine. A healthcare facility client system accesses and/or obtains one or more medical images for output to a main server system for the purpose of developing patient-specific spinal therapy, surgery, and/or procedures. Can be configured as

At block 204, the main server system may be configured to receive and/or access one or more medical images from the healthcare facility client system. In some embodiments, for example, the one or more medical images can be electronically transmitted via the Internet. In other embodiments, the one or more medical images can be input to the main server system offline, for example, via a portable electronic storage medium, image film, or the like.

In embodiments where a CT and/or MRI scan is utilized, a complete three-dimensional reconstruction of the spine can be obtained directly from the CT and/or MRI scan itself. This can be used for further analysis, such as determining one or more parameters and/or variables from medical images to generate appropriate and/or patient-specific spinal treatments, surgery, and/or procedures. Can be useful. However, CT or MRI scans are not available to all patients due to the added cost and general availability of such a scheme.

Rather, in some embodiments, the system provides one or more radiographs that are more widely available to produce the appropriate and/or desired spinal treatment, surgery, and/or procedure specific to a patient. Can be used to determine one or more parameters and/or variables. In some embodiments, the system provides one or more two-dimensional X-ray images, such as sagittal view, front view, flexion, and/or stretch image, separately and/or for further analysis. Configured to determine one or more parameters and/or variables. In certain embodiments, the system can be configured to combine one or more two-dimensional radiographs, such as those described above, to obtain a three-dimensional reconstruction of the spine for further analysis. For example, the system can be configured to combine front and sagittal X-rays for flexion and/or extension. In other embodiments, the system can be configured to utilize a combination of one or more x-ray, MRI, and/or CT images.

To efficiently utilize the multiple medical images, the system may be configured to calibrate the multiple medical images at block 206. More specifically, a common reference point in multiple medical images can be identified and used for calibration and analysis. The system can be configured to identify common reference points in multiple medical images automatically and/or with manual input by the user. For example, the center point of the sacral endplate or vertebra can be identified as a common reference point for multiple medical images. In particular, the center point of the sacrum of the frontal X-ray image and the sagittal plane X-ray image can be specified.

Further, in certain embodiments, multiple medical images can be scaled to match between multiple images. The calibration step identifies common portions, portions, and/or shapes of the patient's spine in the plurality of radiographs, and based on such commonality, one or more of the plurality of radiographs. Including adjusting the scale of. For example, if the L3 plate length is selected as a common feature, the L3 length can be measured for each of a plurality of x-ray images and scaled to fit the x-ray image to such a length. it can.

The calibration process or technique can be fully or partially automated. In some embodiments, the calibration and/or adjustment steps can be automated by extracting the colonic spine and/or measuring, for example, a central plate. After calibrating the multiple medical images, the system can be configured to generate a composite three-dimensional image at block 208 for further analysis. In other embodiments, a single medical image or scan is used, so that no calibration is needed.

The system can be configured at block 210 to analyze one or more medical images. Image analysis includes identifying one or more end plates, identifying distances and/or curvatures between the one or more end plates, and one or more lines between the one or more end plates. May be included, and the like. The one or more parameters can be obtained automatically by the system and/or by manual entry. In addition, image analysis may include rotating one or more medical images, eg, one or more portions thereof, eg, about a specified reference point and/or axis, to perform a desired operation on the spine. It may further include modifying by mimicking the results. In some embodiments, one or more image analysis steps can be performed before and/or after image modification, such as rotation of one or more portions of one or more medical images.

Analysis of one or more medical images may depend on many factors, such as parameters and/or variables specified by the surgeon, the literature and/or a previously collected database of systems. Accordingly, as shown in FIG. 2, the system, at block 210, at the time of analyzing one or more medical images, one or more surgeon database 212, literature database 214, planning database 216, and/or surgery database 218. Can be configured to communicate with. Each or all of the surgeon database 212, the literature database 214, the planning database 216, and/or the surgery database 218 can be configured to be continuously and/or periodically updated.

The surgeon database 212 may be tailored to individual surgeons' preferences, such as particular surgeries or choices that particular surgeons regularly utilize or prefer, and the particular spine that may be needed to meet such surgeon preferences. Parameters may be included. Based on such a determination, the system can be configured, for example, to analyze one or more medical images that match the preferences of a particular surgeon.

The literature database 214 may include one or more medical literature related to, for example, spinal surgery. As medical research advances, additional parameters of the spine can be identified as useful in surgical planning. In addition, more real-world patient data will be available, which may include pre- and/or post-operative data. Thus, in certain embodiments, the literature database 214 can be used by the system to identify certain spinal parameters, variables, and/or additional data that the system can utilize to analyze one or more medical images. ..

Planning database 216 and/or surgery database 218 may include internal data for the system. In other words, the plan database 216 may include data related to one or more previous spinal surgery plans developed by the system. Similarly, surgery database 218 may include data related to previous spinal surgery performed, including surgery performed utilizing the system. Such data relating to previously planned and/or performed spinal surgery also provides parameters for the system to identify and/or determine as part of the analysis of one or more medical images. Or can be specified.

After image analysis, the surgery plan can be performed as part of the case simulation at block 220. The surgical plan may include, for example, designing, creating, modifying, and modifying one or more spinal rods, screws, and/or cages to be used for a particular patient based on analysis of one or more medical images. And/or may include selection. More specifically, in certain embodiments, the surgical plan generated by the system includes length, diameter, curvature, angulation, and/or other dimensions of implants such as spinal rods, screws, and/or cages. One or more of For example, the system can be configured to draw a spinal rod of a particular curvature and/or have the user draw the spinal rod as an overlay on one or more medical images. In particular, in some embodiments, the system can be configured to draw a spline on the spine on one or more medical images and obtain an overall measurement of the drawn spine. The system can also be configured to obtain local data such as the angle of each vertebra for analysis. In certain embodiments, the system can be configured to generate a surgical plan that includes design and/or selection of range, root and/or diameter of one or more spinal screws. Further, in certain embodiments, the generated surgical plan may include the design, shape, size, material, etc. of a particular implant such as a cone device or cage.

Similar to image analysis, the system can be configured to communicate with one or more surgeon databases 212, literature database 214, planning database 216, and/or surgical database 218 when performing a surgical plan. For example, the system can be configured to allow for surgeon-specific preferences during planning. The system can also be configured to consider the medical literature. The system can also be configured to take into account its previously determined and performed surgical plan and its internal database containing the surgery. The system identifies one or more previous spinal surgery performed utilizing this system and/or other systems that may be similar, based on age, severity of deformity, bone strength or density, etc. , Can be configured to provide guidance for current patient cases. The system looks in one or more databases to identify similar cases that were previously performed, identify surgical strategies that were previously used in those cases, and identify outcomes to support planning. Can be configured as For example, in some embodiments, the system can take into account the trajectory of screw and/or cage position and/or insertion depth. More specifically, inputs in determining the trajectory and/or insertion depth of a screw from one or more previous surgeries in determining the desired screw length, trajectory, and/or insertion depth for the current surgery. Can be used as In some embodiments, the system can be configured to determine a recommended implant design, positioning, and/or material for a spinal rod, screw, or cage based on previously collected data. For example, previously collected data may be selected from a particular case involving a patient's particular clinical outcome and/or bone quality and/or age.

In some embodiments, the generated surgical plan may include a particular design and/or curvature of the spinal rod for a particular patient, including rod diameter, material, length, curvature, and the like. For example, for the upper vertebrae, the system may recommend the use of cervical thoracic rods. In some embodiments, only one plan is designed and provided to the surgeon. In other embodiments, more than one plan can be designed and provided to the surgeon for final decision. For example, for a single set of medical images of a single patient, the number of surgical plans developed by the system may be 2, 3, 4, 5, 6, 7, 8, 9, and/or 10 plans. Can be included. In some embodiments, the system can be configured to develop and provide three plans to the surgeon. For example, the system can be configured to provide a first plan based on a surgeon input strategy and/or purpose. The second plan generated by the system can be based purely on the scientific literature, and the third plan can be based on the internal data of the surgery previously planned by the system. In particular, analyzing previously collected data to determine which strategy or plan had a high success rate, eg, >90%, based on clinical outcomes such as keratometry measurements on similar patients. You can The system can be further configured to receive a surgeon's choice between one or more plans developed for a single patient. In other words, the system can be configured to generate multiple alternative surgical plans for a single patient.

In some embodiments, the system can be configured to provide case simulation guidance to, for example, a surgeon as part of developing patient-specific spinal therapy, surgery, and procedures. In certain embodiments, the system can be configured to generate one or more surgical plans based on the surgeon's strategy and/or goals previously entered into the system. Additionally or alternatively, the surgical planning design may be data driven, for example, based on scientific literature and/or other data from previous surgery. The scientific literature may be from 10 to 20 or more surgeons and/or may be updated continuously and/or regularly. The scientific literature may also be based on internal data collected from previous cases generated by the system. The system, in some embodiments, may also utilize machine learning to continuously improve its surgical plan based on previous internal data and/or scientific literature. The surgery plan may also be based on big data and may utilize one or more big data processing techniques.

The purposes of spinal reorganization may include, for example, surgeon input guidelines, thoracic kyphosis correction, age-related purposes, and/or additional theories about sitting. For example, spinal growth can be considered during the planning phase for younger patients. Moreover, the surgical balance of a patient may depend on the patient's age. The system's case simulation capabilities include surgeon input guidelines and/or strategies, Smith-Petersen osteotomy (SPO) based strategies, transpedicular wedge osteotomy (PSO) and SPO based strategies, cage-based Based strategies, and/or cage and SPO based strategies, and/or any combination of the above. Additional simulations may include spondylolisthesis correction, chest area compensation mechanisms, and/or on-demand and/or on-demand compression/distraction. Some examples of surgeon input include the type of spinal surgery, screw insertion into a particular area, PSO performance at a certain level, SPO performance at a certain level, cage insertion at a particular area, etc. Is included.

In some embodiments, the system-generated surgical plan may include one or more pre-operative and/or surgical plan images. 5 and 6 show exemplary embodiments of case simulation delivery for developing patient-specific spinal therapy, surgery, and procedures. As part of the planning process, as shown in FIGS. 5 and 6, the system can be configured to produce one or more planning images, including a spinal rod, one or more screws, and/or One or more cages can be shown over the one or more modified or analyzed pre-operative images. For example, as part of the planning process, the system can be configured to divide the medical image into one or more parts and rotate such parts relative to each other to obtain postoperative results or depictions of interest. In certain embodiments, the system is configured to identify each vertebra and rotate it to obtain post-operative estimation results or spinal curvature. Rotation of each vertebra and/or one or more portions of the medical image that may include one or more vertebrae may be data driven based on previously acquired data, and/or in certain embodiments, , Can be automated. An overlay of spinal rods can be added over such planning images to obtain a visible depiction of the surgical plan.

The surgical plan also includes one or more preoperative and/or planned pelvic tilt (PT), pelvic morphology (PT), sacral tilt angle (SS), lumbar lordosis (LL), PI-LL, posterior chest. Spinal pelvic parameters such as curvature (TK), T1 pelvic angle, and/or sagittal vertical axis (SVA) may be included. The surgical plan may also include implant requirements such as one or more cervical parameters, surgical steps, screws, and/or rod types.

Returning to FIG. 2, the system may electronically transmit the generated surgical plan or plans or otherwise allow a user to view or access the surgical plan or plans. Can be configured to For example, in some embodiments, the user access point system may be configured to receive one or more surgical plans and/or steps thereof at block 222. The user access point system can be a personal computer (PC), smartphone, tablet PC, or any other electronic device used by one or more medical personnel. The one or more generated plans can be sent to one or more medical personnel, such as by email, application messages or notifications.

The user access point system may be configured to display one or more surgical plans and/or their steps at block 224. In some embodiments, a PDF or other visible copy of the actual surgical plan can be sent to the user access point system for viewing. For example, FIG. 5 illustrates an example of a surgical plan in the form of PDF memos that may be sent to medical personnel by one or more electronic communication methods such as email. In other embodiments, the link can be sent to a healthcare provider or user access point system. By enabling the link, the user access point system can be configured to display one or more generated surgical plans, eg, on a web page or application page. For example, FIG. 6 illustrates an example system platform surgery plan such as a web page or application page that may be viewed by one or more medical personnel through the system.

As briefly discussed above, a single surgical plan may include one or more steps such as PSO, SPO, cage insertion, and/or screw insertion. Each step of the planning may be associated with a particular surgery and/or outcome starting from the pre-operative state. For example, a surgical plan may include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more steps. All steps of the surgical plan can be made visible to the user by the system. In some embodiments, the system and/or user access point system allows a user to "disable" one or more steps, eg, in sequence. In another embodiment, the system allows the user to "disable" all steps of the plan at one time to see substantially the pre-operative condition.

In some embodiments, image analysis, case simulation, and/or surgical planning can utilize one or more three-dimensional medical images to generate a three-dimensional plan. In certain embodiments, one or more two-dimensional medical images can be combined or otherwise utilized to generate a three-dimensional plan. For example, one or more x-ray images from different fields of view, such as the sagittal plane and/or the front view, can be combined to produce a three-dimensional composite image, which in turn produces a three-dimensional surgical plan. Available to generate. More specifically, in some embodiments, the front and sagittal X-ray images are combined using one or more scaling methods discussed above to provide scaling and/or adjustment. The composite three-dimensional image can be formed later. In some embodiments, the generated three-dimensional plan is used to provide one or more desired for one or more patient-specific spinal implants, such as three-dimensional spinal rods, cages, and/or screws. Parameters and/or variables can be determined.

Other patient-specific implants, such as cages, can be similarly designed, selected, and/or made using the systems, methods, and devices described herein. In particular, CT scans and/or three-dimensional printing can be used to design, select, and/or create patient-specific cages. One or more features or techniques for synthetic radiographs described herein can also be used in the development of patient-specific cages. The EOS X-ray imaging system can also be used in connection with any of the embodiments herein.

Returning to FIG. 2, in some embodiments, the user access point system may be configured at block 226 to receive input from one or more medical personnel regarding the generated one or more plans. .. For example, one or more medical personnel may select and/or validate one of the multiple surgical plans developed by the system. Additionally and/or alternatively, one or more medical personnel may modify a particular surgical plan, for example, by inserting comments and/or changing particular parameters and/or variables. Can require that. The user access point system can be configured to relay or send such input to the main server system, which is based on the input received from one or more medical personnel at block 228. Or it can be further configured to modify multiple surgical plans. At block 230, the system may be configured to present the final surgical plan to one or more medical personnel, such as a surgeon, via the user access point system.

Then, in certain embodiments, the system creates, modifies, and/or selects one or more patient-specific spinal rods, cages, and/or screws for implantation, eg, from an existing inventory. Can be configured as For example, the system may create, modify, and/or select one or more spinal rods that may bend or otherwise be desirable and/or specific to a particular patient prior to surgery. Can be configured. One or more spinal rods can be configured for use in minimally invasive surgery (MIS). In certain embodiments, an added benefit may be that real-time x-rays during surgery are minimized or not used altogether to minimize irradiation by the patient. Further, in some embodiments, the system creates, modifies, and/or prior to surgery one or more cages and/or screws that are desirable and/or otherwise specific for a particular patient. It can be configured to select.

FIG. 3 is a flow chart illustrating an exemplary embodiment of implant preparation, case support, and data collection for developing patient-specific spinal therapy, surgery, and procedures. In some embodiments, the implant fabrication and/or computing facility computing system accesses and/or the final surgical plan or surgical plans at block 302 via, for example, the Internet or a portable electronic storage medium. It can be configured to receive it. The implant fabrication facility may be configured to fabricate, modify, and/or select one or more components for surgery at block 304. For example, an implant fabrication facility can be configured to fabricate spinal rods, cages, and/or screws based on specifications and/or materials specified in a surgical plan. Similarly, the implant fabrication facility may select and/or select one or more prefabricated spinal rods, cages, and/or screws based on specified values and/or materials specified in one or more surgical plans. Or it can be configured to modify.

In some embodiments, the spinal rods, cages, and/or screws can be made of one or more different materials. The particular material to be used for a particular patient-specific rod, screw, and/or cage may depend on the data and/or may be selected by the surgeon. The particular material may also depend, inter alia, on the height, weight, age, bone density, and/or bone strength of the particular patient, among others.

7A and 7B illustrate a spine that can be made and/or selected from a range of existing spinal rods using certain embodiments of systems, devices, and methods for patient-specific spinal therapy, surgery, and procedures, for example. 3 illustrates an exemplary embodiment of a rod. In some embodiments, the system can be configured to design, select, and/or create one or more thoracolumbar, cervical chest rods, MIS rods, and/or 3D vent rods. In certain embodiments, spinal rods can be made from titanium, cobalt-chromium alloys, and/or any other material.

As discussed above, in some embodiments, the system can be configured to make, select, and/or modify rods that can be bent in one or more directions. It is usually difficult, if not impossible, for the surgeon to bend the rod in one direction, if not more than one, with tools before or during surgery. possible. In contrast, by utilizing the synthesis of two-dimensional X-ray images and/or three-dimensional medical images, the system can be bent in two or more directions, for example in the lateral direction as well as in the sagittal plane direction, or The curved rod can be made and/or configured to be selected from an existing inventory.

FIG. 7C illustrates an exemplary implementation of a cage that can be made and/or selected using, for example, an existing range of cages using certain embodiments of systems, devices, and methods for patient-specific spinal therapy, surgery, and procedures. The morphology is shown. FIG. 7D illustrates an exemplary implementation of a screw that can be made and/or selected, for example, from an existing range of screws using certain embodiments of systems, devices, and methods for patient-specific spinal therapy, surgery, and procedures. The morphology is shown.

Returning to FIG. 3, at one or more medical personnel, at block 306, a spinal rod, created, modified and/or selected by an implant fabrication facility based on the surgical plan at block 304, for implantation. It can be selected from one or more implants such as cages and/or screws.

In some embodiments, one or more medical personnel can attach and/or enable one or more sensors for intraoperative tracking at block 308. One or more sensors can be placed on one or more screws and/or nuts for attachment to a patient's vertebra and/or can be placed on a tool for attachment to the same vertebra. One or more sensors that can be used in certain embodiments are discussed in more detail below. In some embodiments, the sensors may be placed in or attached to all vertebrae for spinal surgery. This can be advantageous to get accurate data. However, this may not be desirable in some situations due to the size of the data. For example, if vertebral angle may be one of the most important parameters, a large amount of unnecessary data may be collected. Thus, the sensor can be attached only to a subset of vertebrae that can obtain useful position and/or angle data of the spine.

In some embodiments, the system is implanted in the vertebrae, assuming that the implanted screw is parallel to the end plate, for example, to obtain intraoperative tracking, instead of relying on image processing techniques. It may be configured to utilize data collected from one or more sensors inside or attached to one or more screws. In other words, the angulation of the screw in the sagittal plane can be assumed to be equal or substantially equal to the vertebral angulation. In some embodiments, the top of the screw can include active or passive sensors. In some embodiments, the apex can be later cut off during surgery, thereby reusing the sensor. One or more screws containing one or more sensors can be inserted into all vertebrae or a subset thereof. For example, in some embodiments, the sensor can be attached to all 20 vertebrae. In other embodiments, the sensors can be attached to only a subset thereof, eg, two or more sensors attached to the upper lumbar spine and/or two or more sensors attached to the lower vertebra. The sensor is then used to obtain data relating to the position and/or angle or orientation of the vertebra in six degrees of freedom (or nine degrees of freedom) in translation and rotation in real time, near real time and/or substantially real time. Available for Raw data collected by one or more sensors can be transmitted to a computer system to convert the raw data into tracking positions and/or orientations of one or more vertebrae, such as spinal curvature and/or surgical Can help determine the correction.

Based on real-time, near-real-time, and/or substantially intraoperative tracking or monitoring, the system can be configured to track the position and/or orientation or angulation of the vertebra and/or screw. In other words, in some embodiments, spinal correction during surgery can be monitored in real time, near real time, and/or substantially real time. Returning to FIG. 3, in certain embodiments, tracking data corresponding to the position and/or angulation of each vertebra can be transmitted to the main server system and/or client system at the healthcare facility in block 310.

In certain embodiments, at block 312, the main server system and/or the healthcare facility client system, after one or more medical personnel have inserted one, two, or more screws into the patient's spine. , Can be configured to track, analyze, and/or store different vertebral movements during correction and other surgical procedure data. One or more medical personnel can thus visualize or otherwise track the position, orientation, correction and/or angulation of the vertebrae in real-time, near real-time, and/or substantially real-time, under desirable conditions, For example, it is possible to specify when a condition that matches a predetermined surgery plan is obtained.

Such live tracking can provide substantial support to healthcare professionals. For example, without intraoperative follow-up, the surgeon would think that if a PSS were performed, a 30 degree correction could be obtained; however, in practice, if the PSS performed would result in only a 10 degree correction. There is. In such situations, allowing intraoperative tracking or monitoring allows the surgeon to make further corrections as needed before stopping the surgery.

In certain embodiments, the system can be configured to perform an analysis of tracking data by comparing the tracking data against a given surgical plan. To do so, the system can retrieve data from planning database 216 and/or surgery database 218. Based on such comparisons and/or analysis, the system may be configured to dynamically generate and/or provide guidance to the surgeon during surgery, in real time and/or near real time, at block 314. For example, the system can be configured to instruct or guide the surgeon to vary the angle of one or more vertebrae to obtain a spinal curvature near a predetermined plan based on the tracking data.

The system can be further configured to provide audible and/or visual notifications and/or guidance to the surgeon. Audible and/or visual notification and/or guidance may include instructions to the surgeon to perform the surgery in a particular manner or angle, and/or the location and/or angulation of one or more vertebrae. The surgeon can be notified if within a predetermined threshold. For example, the system may have one or more screws and/or vertebra locations and/or angulations of about 1%, about 2%, about 3%, about 4%, about 5%, about 10% of a given plan. %, about 15%, about 20%, about 25%, and/or within a range determined by two of the above values, notifications may be provided. In certain embodiments, the system can be configured to provide a visual depiction of the position, location, orientation, and/or angulation of each vertebra on the display based on the tracking data to guide the surgeon.

Once an acceptable level of horned vertebrae is obtained, the surgeon inserts a spinal rod and/or tightens screws on the rod to secure all components and complete the surgery, eg, at block 316. In certain embodiments, the surgeon can then remove and/or disable one or more sensors at block 318.

In certain embodiments, the system can be further configured to collect post-operative data and/or utilize the post-operative data to provide predictive modeling and/or other post-operative functions or services. Further, in some embodiments, the system can be configured to consider the level of sophistication and/or preference of the surgeon to provide surgeon-specific recommendations for future cases. In certain embodiments, comparison and/or analysis of preoperative, intraoperative, and/or postoperative data and/or surgeon input can be used to determine a surgeon's skill level and/or strategic preference. A surgeon's particular skill level and/or strategic preference can be used to develop subsequent surgical plans for that surgeon. Further, in some embodiments, data related to spinal growth and/or other subsequent development such as curvature-related can be obtained from one or more post-operative radiographs. Such long-term effects can also be used for subsequent planning.

In some embodiments, as part of predictive modeling and/or machine learning, the system can be configured to analyze one or more different plans developed for a particular case. For example, in some embodiments, the initially generated plan may be based on the surgeon's strategy and/or purpose. The second generated plan for the same case may be based on data from the scientific literature. The plan generated a third time for the same case may be based on the data previously collected by the system through performing surgery. As more data is collected, and more feedback and input is given and obtained from the surgeon, and/or more scientific research is conducted, a single case One or more generated plans and/or their particular features may converge. Certain parameters, which tend to be more prominent than others, are more available by the system in subsequent planning stages. Further, in certain embodiments, the system can be configured to compare a given case with a previous case at the planning stage. For example, the system may analyze one or more databases to find one or more spines that match a given case and/or its particular characteristics, and make specific recommendations and/or predictions for the plan. Can be configured to

FIG. 4 is a flow chart illustrating an exemplary embodiment of data collection, machine learning, and predictive modeling to develop patient-specific spinal therapy, surgery, and procedures. In some embodiments, the system can be configured to retrieve preoperative, intraoperative, and/or postoperative data from the planning database 216 and/or the surgical database 218 at block 402.

In particular, in some embodiments, the system is adapted to collect one or more sets of data including one or more quantitative parameters and/or one or more radiographs or other medical images. Can be configured. The one or more quantitative parameters are pelvic tilt (PT) for each or one or more vertebrae, and for each or one or more preoperatively, at the planning stage, and/or postoperatively. , Pelvic morphology (PT), sacral inclination (SS), lumbar lordosis (LL), PI-LL, thoracic kyphosis (TK), T1 pelvic angle, and/or sagittal vertical axis (SVA). .. The one or more radiographs may be preoperative, planned, postoperative, and/or one or more years postoperative. The collected data can be used to build a database for future reference, for example, for the purposes of data-driven and/or partially data-driven planning.

In some embodiments, post-operative data can be collected on a continuous and/or regular basis. For example, one or more medical images of the patient's spine, such as post-operative data, one or more parameters or variables thereof, and/or an x-ray image, may be obtained 3 months postoperatively or 6 months postoperatively. At or near months, 1 year or near postoperative, 2 years or near postoperative, 3 years or near postoperative, 4 years or near postoperative, 5 years or near postoperative. , 10 years postoperatively, 15 years postoperatively or near, 20 years postoperatively or near postoperative, and/or between any of the above values, and/or two of the above values. It can be collected within the range determined by.

In particular embodiments, the system may be further configured to retrieve surgeon input data and/or literature-based data at block 404. Surgeon-based data and/or literature-based data can be retrieved from surgeon database 212 and literature database 214, as described above.

In some embodiments, the system can be configured to analyze pre-operative, intra-operative, and/or post-operative data at block 406 and/or generate reports or other statistical data at block 408. For example, in some embodiments, the system can be configured to compare one or more radiographs from the planning phase, the surgical phase, and the post-operative phase. To obtain an accurate comparison, the system uses one or more X-ray images taken from different time points in a manner similar to that described above in connection with the calibration of one or more X-ray images from different fields of view. Can be configured to calibrate. For example, a common reference point, such as the center point of the sacral end plate, can be selected for each X-ray image taken from different time points. The length of the common feature value, such as plate length, can also be specified and used as a scale for scaling the X-ray image.

Based on the calibrated radiograph, the system can be configured to generate a report including a preoperative, intraoperative, and/or postoperative spinal overlay of the patient. One or more postoperative radiographs may be, for example, 1 month after surgery, 6 months after surgery, 1 year after surgery, 2 years after surgery, 3 years after surgery, 5 years after surgery, 10 years after surgery, and so on. X-ray images taken at different times and/or their overlays can provide a visual indication of how closely the surgery was performed and/or how the surgical outcome was close to the surgical plan.

The generated report may further include one or more sacral parameters such as SVA, PI, LL, PI-LL, PT. The generated report may also include a percentage of achievement as compared to the surgical plan. Therefore, by comparing the pre- and post-operative data, data relating to the strengths and/or weaknesses of the outcome of the surgical plan can be obtained. In addition, pre- and intra-operative data may be compared to obtain data relating to the strengths and/or weaknesses of a particular surgeon's practice.

The system can be configured to send the generated reports and/or statistical data to the surgeon who performed the surgery. For example, the surgeon's user access point system may receive such generated reports and/or statistical data at block 410. After review, the surgeon may provide input via the user access point system at block 412. For example, the surgeon may provide input that a particular procedure and/or selection can be improved. The surgeon may also give and/or change general settings based on the report. All such data can be stored in the system's database for future reference. Such analysis can then be used at block 414 for subsequent preoperative planning and/or case simulation.

In certain embodiments, the system can also be configured to utilize machine learning techniques or processes. For example, the system can be configured to learn from previous plans and surgery based on classification of patient severity, age, weight, height, and/or any other personal characteristic. The system can be configured to extract data from such machine learning databases and/or processes.

Overview of Cage/Screw Planning As discussed herein, in certain embodiments, the system designs, creates, modifies, and/or provides guidance for one or more patient-specific screws and/or cages. Alternatively, it may be provided and configured to increase the effectiveness and/or control the cost of spinal surgery. For example, one or more patient-specific screws and/or cages can be selected from an existing range or inventory of screws and/or cages. In particular, in some embodiments, the system is specific at a specific anatomical location, such as a particular vertebra and/or intervertebral space, based at least in part on analysis of one or more medical images. It can be configured to design, make, modify, and/or provide guidance for the selection of one or more screws and/or cages to be used for the patient. For example, the system can be configured to design a specific screw for insertion into a specific vertebra and/or a specific cage for insertion into a specific intervertebral space.

In particular, in some embodiments, the system determines one or more, for example, determined in part based on analysis of one or more x-ray images, MRI slices, and/or other medical images. It may be configured to provide and/or recommend screw selection and/or one or more design parameters and/or their dimensions, including screw diameter and/or length. Similarly, in some embodiments, the system may determine, for example, a footprint, posterior, determined in part based on analysis of one or more x-ray images, MRI slices, and/or other medical images. It can be configured to provide and/or recommend one or more design parameters and/or their dimensions, including height, anterior height, width, length and/or lordosis.

In certain embodiments, the surgical plan and/or image analysis output generated by the system may include one or more of the length, diameter, size, and/or angulation of implants such as cages and/or screws. .. More specifically, in some embodiments, the system may determine and/or generate a screw of a desired length and/or diameter, for example, for insertion into a particular vertebra of a particular patient. Can be configured to. In certain embodiments, the system may be used for specific interbody fusion, such as a cage, of desired design, shape, size, and/or angulation, eg, for insertion into a particular intervertebral space of a particular patient. It can be configured to determine and/or generate a device.

Thus, in some embodiments, the system is designed to ensure an optimal and/or desired correspondence between one or more implants and a specific anatomy of a given patient. It can be configured to design and/or determine one or more desired parameters of a patient-specific spinal cage and/or screw based on one or more standard values of the plan. Thus, in certain embodiments, this process can reduce associated costs by reducing the need for inventory of screws and/or cages that need to be manufactured and/or held in stock. The associated costs can be further reduced because inventory sterilization costs are reduced. Additional benefits may be associated with reduced surgical time and simplified surgery. For example, in some embodiments, the system can be configured to determine and/or generate certain criteria and/or dimensions of screws and/or cages that are as acceptable as possible for a particular patient. Based on such a determination, in certain embodiments, a personalized caddy including a less selective cage and/or screw that is determined prior to surgery may be provided at the time of surgery.

FIG. 8 is a flow chart illustrating an exemplary embodiment of cage and/or screw design, fabrication, modification, and/or selection. As shown in FIG. 8, in some embodiments, the healthcare facility client system is configured to access and/or obtain one or more two-dimensional and/or three-dimensional medical images at block 802. It The one or more medical images may include one or more sagittal and/or frontal X-ray images, flexion and/or extension X-ray images, CT images, MRI images, and/or one or more other It may include medical images acquired using imaging techniques. In a particular embodiment, the main server system, at block 804, provides one or more 2D and/or 3D medical images to a healthcare facility client, eg, in a manner similar to that described in connection with FIG. It can be configured to receive from the system. In particular, one or more feature values described in connection with FIG. 2 relating to receiving and/or accessing medical images may be similarly applicable to the one or more steps shown in FIG. ..

In some embodiments, the system calibrates and/or scales the one or more two-dimensional and/or three-dimensional medical images at block 806, eg, similar to that described in connection with FIG. , Block 808 may be configured to generate one or more synthetic medical images. In particular, one or more of the feature values described in connection with FIG. 2 relating to calibration and scaling of a medical image and generation of a composite image thereof are similar to the one or more steps shown in FIG. May be applicable.

In particular embodiments, the system may be further configured to perform one or more analyzes of the one or more medical images at block 810. For example, in some embodiments, the system may include posterior longitudinal ligament (PLL)-based analysis, anterior longitudinal ligament (ALL)-based analysis, and/or intermediate for planning and/or designing a patient-specific cage. It can be configured to utilize plate-based analysis. This will be described in more detail below.

In some embodiments, analysis of one or more medical images for cage and/or screw planning may also include prior acquisition of surgeons, surgeon preferences, spinal anatomical data, literature and/or systems. Can depend on many factors such as parameters and/or variables specified by the surgical database. Thus, as shown in FIG. 8, the system, at block 810, at the time of analyzing one or more medical images, one or more surgeon databases 212, literature databases 214, planning databases 216, and/or surgery databases 218. Can be configured to communicate with. Each or all of the surgeon database 212, the literature database 214, the planning database 216, and/or the surgery database 218 can be configured to be continuously and/or periodically updated.

In certain embodiments, the system generates one or more desired parameters at block 812 for the patient-specific cage and/or screw based at least in part on the analysis performed at block 810. Can be configured as For example, in some embodiments, the system specifies one or more of the following patient-specific parameters for one or more screws used in spinal surgery: root, length, and/or diameter. Can be configured to Further, in certain embodiments, the system provides the following patient-specific parameters for the implanted cage or cages: type, design, shape, size, material, width, height, angulation, orientation, and And/or can be configured to specify one or more of the lengths.

In some embodiments, the computing system at the implant fabrication facility and/or the healthcare facility client system is configured to generate the desired one or more parameters for the patient-specific cage and/or screw, for example, It can be configured to access and/or receive via the Internet or portable electronic storage media. In a particular embodiment, at block 814, the implant fabrication and/or selection facility and/or healthcare facility client system determines one or more based at least in part on the one or more desired parameters generated. Can be configured to make, modify, and/or provide guidance for patient-specific screw and/or cage selection of the.

Cage Planning-Posterior Approach In certain embodiments, the system can be configured to provide cage planning and/or cage design, for example, as part of case support. The system can be configured to determine a particular cage that may fit a particular patient and recommend one or more such cages. More specifically, in some embodiments, the system ensures that the spine of a particular patient is not over distracted and/or does not attempt to correct the spine beyond its physiological capacity, It can be configured to determine and/or focus on the length of one or more ligaments. More specifically, cage selection may be important not to select cages that may cause overextension of the spine beyond the length of the longitudinal ligament of a particular patient. Thus, in some embodiments, the system is configured to measure one or more dimensions of one or more longitudinal ligaments rather than simply measuring the straight length along the spinal column. It

FIG. 9A is a flow chart illustrating an exemplary embodiment of cage design, fabrication, modification, and/or selection. As shown in FIG. 9A, in some embodiments, the system can be configured to utilize a posterior or posterior-anterior approach. More specifically, in certain embodiments, the system may access and/or obtain at block 902, for example, one or more two-dimensional and/or three-dimensional medical images of a patient's spine. Composed. The one or more medical images may include one or more sagittal and/or frontal X-ray images, flexion and/or extension X-ray images, CT images, MRI images, and/or one or more other It may include medical images acquired using imaging techniques.

In some embodiments, the system can be configured to measure the length of one or more ligaments from one or more medical images. In some embodiments, to take into account maximum length, the system may determine the stretched ligament length from, for example, one or more medical images acquired when the patient is in a stretched state. Composed. In certain embodiments, the system can be configured to measure the anterior and/or posterior ligament length, which constitutes both horizontal and vertical lengths.

More specifically, in certain embodiments, the system is configured to examine a patient's posterior longitudinal ligament (PLL) and/or anterior longitudinal ligament (ALL) from one or more MRI images and/or radiographs. Can be configured to determine the length of one or more ligaments, such as. The one or more x-ray images can be dynamic, such as flexion and/or stretch x-rays. In the embodiment illustrated in FIG. 9A, the system, at block 904, the PLL and/or ALL lengths of a particular spinal segment of the patient on one or more MRI, X-ray and/or dynamic radiographs. Although it can be configured to determine the height, in certain embodiments this step can be optional.

In certain embodiments, the system can be configured to obtain the length of one or more ligaments, such as ALL and/or PLL, by direct measurement of the ligaments from one or more MRI images. FIG. 9B illustrates an exemplary embodiment of cage design, fabrication, modification, and/or selection. In particular, in the exemplary embodiment of FIG. 9B, an MRI image 920 is provided on which the system is configured to determine the length of both ALL and PLL. In the example shown, the PLL length for a particular spinal segment is determined to be approximately 61.5 mm and the ALL length for the same specific spinal segment is approximately 64.3 mm.

Further, in some embodiments, the system can provide a range of motion of one vertebra relative to another from one or more radiographs and/or dynamic radiographs to account for one intervertebral space. Can be configured to estimate and/or determine the length of one or more ligaments, such as ALL and/or PLL. For example, from an X-ray image taken when the patient is in flexion, the system can be configured to estimate and/or determine the PLL length for a particular spinal segment of the patient.

Thus, returning to FIG. 9A, in certain embodiments, the system may, at block 906, determine and/or estimate the length of the particular patient's PLL along the particular spinal segment on the flexion radiograph. Can be configured to. FIG. 9B further shows a flexion x-ray image 916, to which the system is configured to estimate and/or determine the length of the PLL along a particular spinal segment. In the example shown, the length of the PLL along a particular spinal segment is determined to be approximately 61.5 mm.

Returning to FIG. 9A, in some embodiments, the system may, at block 908, determine and/or estimate the length of the ALL for a particular patient along a particular spinal segment on the stretched radiograph. Can be configured. FIG. 9B further shows a stretched x-ray image 918 to which the system is configured to estimate and/or determine the length of the ALL along a particular spinal segment. In the example shown, the length of the ALL along a particular spinal segment is determined to be approximately 64.3 mm.

Based at least in part on such measurements taken from one or more MRI images and/or X-rays or dynamic X-ray images, the system determines and/or considers the anterior and posterior heights of the cage. As such, and in certain embodiments, can be further configured to further consider angulation and/or positioning in the design and/or determination of one or more patient-specific cages. Thus, in certain embodiments, by considering the assessment of one or more ligaments, the system can ensure that the generated surgical plan does not overextend the patient's spine.

More specifically, returning to FIG. 9A, the system, at block 910, one or more anterior curvatures () along the anterior column on an x-ray image, such as a postural x-ray image, for further analysis. AC) and/or one or more posterior curvatures (PC) along the posterior column can be overlaid, drawn, and/or configured to allow the user to overlay and/or draw it. In certain embodiments, the system automatically and/or for example, one or more bends along the AC and/or PC without displaying and/or overlaying the bends on a display, for example. It can be configured to identify semi-automatically.

FIG. 9C illustrates an exemplary embodiment of cage design, fabrication, modification, and/or selection. In particular, in the exemplary embodiment shown in FIG. 9C, a postural X-ray image 922 is provided on which the system can be configured to simulate the implantation of a cage having particular dimensions for the spinal segment. In the example shown, in particular, the system can be configured to overlay the posterior curve and/or the anterior curve. In the illustrated example, the PC is identified and/or drawn as passing through the corners of the four posterior vertebrae, and the AC is identified and/or drawn as passing through the corners of the four anterior vertebrae. In the example shown, the AC for the particular spinal segment of interest is determined to be approximately 59.4 mm and the PC for the particular spinal segment of interest is determined to be approximately 56.5 mm.

Returning to FIG. 9A, in some embodiments, the system, at block 912, until the length of the PC is equal to the length of the PLL on the x-ray image, such as a postural x-ray image, for further analysis. It can be configured to increase the posterior height (Hpost) of one or more cages. Thus, in a particular embodiment, at block 912, the system draws the AC and/or PC by increasing the Hpost of the cage proposed for implantation until the length of the PC equals the length of the PLL. It can be configured to correct an X-ray image of the body posture. In certain embodiments, the system may automatically and/or semi-automatically, for example, without displaying and/or overlaying Hpost on a display until the length of the PC equals the PLL. Can be configured to increase the Hpost of the cage. In some embodiments, the system can be configured to increase the Hpost of one or more cages while at the same time ensuring that the PCs are shorter than, equal to, or substantially equal to the PLL.

FIG. 9C further shows a postural X-ray image 924, to which the system displays Hpost of the proposed cage in a particular intervertebral space for implantation, PC length PLL length. It is configured to increase until it equals. In the example shown, the proposed cage Hpost is increased until the PC curve length and PLL length are determined and/or estimated to be approximately 61.5 mm. Further, in the example shown, the AC length is determined to be approximately 62.1 mm and the ALL length is determined to be approximately 64.3 mm.

Returning to FIG. 9A, in some embodiments, the system, at block 914, increases the lordosis of the spine for a particular spinal segment of interest while at the same time keeping the AC length less than or at least equal to ALL. Can be configured to Thus, in a particular embodiment, at block 914, the system increases the Hpost of the AC and/or PC rendered and/or proposed cage, for example, by increasing the lordosis X. It can be configured to correct the line image. In certain embodiments, the system automatically and/or semi-automatically, eg, without displaying and/or overlaying the AC length on the display, increases the lordosis and simultaneously increases the AC length. It can be configured to hold less than or at least equal to the ALL length.

FIG. 9C further shows a postural radiograph 926, whereas the system augments the lordosis for a particular spinal segment of interest while at the same time keeping the AC length less than or at least equal to ALL. Can be configured as In the example shown, both the PC curvature length and the PLL length are determined and/or estimated to be approximately 61.5 mm, as previously determined in postural radiograph 924. Further, in the example shown, the length of the AC by increasing lordosis is determined to be approximately 64 mm and the length of ALL is determined to be approximately 64.3 mm, as described above. .. Based at least in part on the modified and/or identified PC, PLL, AC, and/or ALL, the system can, for example, maximize spinal correction results without overextending the patient's spine. It may be configured to determine, design and/or estimate one or more dimensions of the patient-specific cage, such as anterior height (Hant), posterior height (Hpost), and/or lordosis. For example, in the example shown, it can be determined and/or inferred that the patient-specific cage should include approximately 10 mm Hant, approximately 7 mm Hpost, and/or approximately 6° lordosis.

Thus, in some embodiments, when designing and/or planning a patient-specific cage, the system may include one or more spinal ligaments, such as ALL and/or PLL, to prevent excessive distraction of the spine. By evaluating and/or analyzing the length, it can be configured to determine, estimate, and/or define the anterior and/or posterior height of one or more patient-specific cages. Further, in certain embodiments, the system also takes into account the positioning of such one or more cages, for example, on postural radiographs or other medical images. It can be configured to determine, estimate, and/or define patient-specific cage cornification. In some embodiments, one or more cage parameters thus determined, such as posterior height, anterior height, overall height, length, width, angulation, etc., are used in the system to, for example, , One or more patient-specific cages can be designed, made, and/or selected from an existing range or inventory.

Cage Planning-Anterior Approach In some embodiments, the system is overextended in the use of this ligament length in the measurement and design of one or more ligament lengths, or as a result of surgery on the patient's spine. It can be configured to utilize an anterior or posterior approach in determining one or more dimensions of the patient-specific cage to ensure absence. FIG. 10A is a flow chart illustrating an exemplary embodiment of cage design, fabrication, modification, and/or selection. In particular, as shown in FIG. 10A, in some embodiments the system can be configured to utilize the anterior approach.

More specifically, in certain embodiments, the system may, at block 1002, access and/or obtain one or more two-dimensional and/or three-dimensional medical images of the patient's spine, for example. Composed. The one or more medical images may include one or more sagittal and/or frontal X-ray images, flexion and/or extension X-ray images, CT images, MRI images, and/or one or more other It may include medical images acquired using imaging techniques.

In some embodiments, the system provides one or more MRI and/or X-ray images from one or more of the patient's posterior longitudinal ligament (PLL) and/or anterior longitudinal ligament (ALL). It can be configured to determine the length of multiple ligaments. The one or more x-ray images can be dynamic, such as flexion and/or stretch x-rays. In the embodiment illustrated in FIG. 10A, the system at block 1004 determines the length of the patient's PLL for a particular spinal segment on one or more MRI, X-ray and/or dynamic radiographs. However, in certain embodiments, this step can be optional.

In certain embodiments, the system can be configured to obtain the length of one or more ligaments, such as ALL and/or PLL, by direct measurement of the ligaments from one or more MRI images. FIG. 10B illustrates an exemplary embodiment of cage design, fabrication, modification, and/or selection. In particular, in the embodiment shown in FIG. 10B, an MRI image 1016 is provided, on which the system is configured to determine the length of the PLL for the spinal segment of interest. In the example shown, the length of the PLL for a particular spinal segment is determined to be approximately 61.5 mm.

Further, in some embodiments, the system can provide a range of motion of one vertebra relative to another from one or more radiographs and/or dynamic radiographs to account for one intervertebral space. Can be configured to estimate and/or determine the length of one or more ligaments, such as ALL and/or PLL. For example, from a flexion radiograph, the system can be configured to estimate and/or determine the PLL length for a particular spinal segment of the patient.

Thus, returning to FIG. 10A, in certain embodiments, the system may, at block 1006, determine and/or estimate the length of the particular patient's PLL along the particular spinal segment on the flexion radiograph. Can be configured to. FIG. 10B further shows flexion radiograph 1014, to which the system is configured to estimate and/or determine the length of the PLL along a particular spinal segment. In the example shown, the length of the PLL along a particular spinal segment is determined to be approximately 61.5 mm.

In some embodiments, the system includes one or more anterior curvatures (AC) along the anterior column and/or 1 along the posterior column (PC) on an x-ray image, such as a postural x-ray image. It may be configured to overlay and/or draw one or more posterior flexures (PCs) and/or have a user overlay and/or draw it. Returning to FIG. 10A, in certain embodiments, the system, at block 1008, overlays, draws, and/or presents to the user one or more posterior curvatures for the particular spinal segment of interest. It can be configured to overlay and/or draw. In certain embodiments, the system is semi-automatic, automatically and/or without displaying and/or overlaying one or more posterior curves and/or anterior curves. Can be configured to be specific.

FIG. 10C illustrates an exemplary embodiment of cage design, fabrication, modification, and/or selection. In particular, in the exemplary embodiment shown in FIG. 10C, a postural X-ray image 922 is provided on which the system can be configured to simulate the implantation of a cage having particular dimensions for the spinal segment. In the example shown, in particular, the system can be configured to overlay a PC. In the example shown, the posterior curvature is identified and/or drawn as passing through the corners of the four posterior vertebrae. In the example shown, the posterior curvature for the particular spinal segment of interest is determined to be approximately 56.5 mm.

Returning to FIG. 10A, in some embodiments, the system can be configured to apply and/or augment the lordosis of the cage at block 1010. Thus, in certain embodiments, at block 1010, the system is configured to further modify the postural radiograph in which the PC curve is depicted by applying and/or increasing the lordosis of the simulated cage. it can. In certain embodiments, the system applies and/or augments the lordosis of the cage automatically and/or semi-automatically, for example, without displaying and/or overlaying a curve on the display. Can be configured.

FIG. 10C further shows a postural radiograph 1020 to which the system is configured to apply and/or augment the lordosis of the cage. In the example shown, by applying the lordosis of the cage to a particular intervertebral space, the lordosis of the cage is approximately 9° and the PC for the spinal segment of interest is approximately 54.7 mm. You can decide.

Returning to FIG. 10A, in some embodiments, the system, at block 1012, increases the height of multiple cages until the PC equals the length of the PLL on an x-ray image, such as a postural x-ray image. , Overall, can be configured to increase. Thus, in certain embodiments, at block 1012, the system increases the overall cage height for a particular intervertebral space until the PC curve length equals the length of the PLL for the particular spinal segment. This can be configured to correct the postural radiograph with the PC curve drawn and/or the cage lordosis applied. In a particular embodiment, the system allows the total cage height to be automatically and/or without displaying and/or overlaying the total cage height on the display, for example, until the PC equals the PLL. It can be configured to apply and/or augment semi-automatically. In some embodiments, the system can be configured to increase overall cage height while at the same time ensuring that the PCs are shorter than, equal to, or substantially equal to the PLL.

FIG. 10C further shows a postural X-ray image 1022, for which the system is configured to increase the overall height for the cage in a particular intervertebral space until PC equals PLL. It In the example shown, after increasing the cage height globally until the PC length equals the PLL length for the particular spinal segment of interest, the proposed cage Hant is approximately 12 mm. The Hpost of the proposed cage is determined to be approximately 8 mm. Furthermore, in the example shown, the lordosis is still maintained at 9° and the PC and PLL lengths are determined to be approximately 61.5 mm accordingly after increasing cage height. ..

Thus, in some embodiments, when designing and/or planning a patient-specific cage, the system evaluates and/or evaluates the length of a single spinal ligament, such as an ALL or PLL, to prevent hyperdistension of the spine. Alternatively, the analysis can be configured to determine, estimate, and/or define the anterior and/or posterior heights of one or more patient-specific cages. Further, in certain embodiments, the system also takes into account the positioning of such one or more cages, for example, on postural radiographs or other medical images. It can be configured to determine, estimate, and/or define patient-specific cage cornification. In some embodiments, one or more cage parameters thus determined, such as anterior height, posterior height, overall height, length, width, angulation, etc., are used in the system to, for example, , One or more patient-specific cages can be designed, made, and/or selected from an existing range or inventory.

Cage Planning-Intermediate Plate Analysis In some embodiments, the system designs or determines one or more dimensions of a patient-specific cage so that the patient's spine does not overextend or overextend, for example, as a result of surgery. To ensure that, the curvature defined by the medial position of each vertebral endplate can be analyzed and/or otherwise configured to focus on it. FIG. 11A is a flow chart illustrating an exemplary embodiment of cage design, fabrication, modification, and/or selection. FIG. 11B is a schematic diagram illustrating an exemplary embodiment of cage design, fabrication, modification, and/or selection. In particular, as shown in FIGS. 11A and 11B, in some embodiments the system can be configured to utilize an intermediate plate approach.

More specifically, in certain embodiments, the system can be configured to measure the height of one or more discs along a curve that intersects the medial position of the vertebral endplates. Based on the determined one or more intervertebral disc height measurements, the system may perform a total measurement along the spine or a portion of the spine, for example, along the lumbar spine as shown in the exemplary embodiment of FIG. 11B. It can be configured to calculate an average distribution of each disc height as a percentage of disc height. In some embodiments, the system can be configured to provide undulations of one or more intermediate plate locations along the location and/or curvature. Such data may be compared to previous cases, such as to design and/or determine one or more dimensions, such as overall height and/or horning of one or more patient-specific cages. Data can be collected and/or compared to scientific literature for predictive planning purposes

In some embodiments, the system can be configured to utilize data driven techniques and/or cage planning processes. In particular, in certain embodiments, the system can be configured to compare and/or analyze such percentages with data obtained from a healthy and/or asymptomatic population and/or with existing data. In particular, in certain embodiments, if the calculated percentage of a particular disc height for a particular patient differs from a healthy population value, the system may indicate that a cage needs to be implanted for that disc. Can be configured to determine. Based on such an analysis, the system determines one or more dimensions for the patient-specific cage for the disc and determines the acceptable disc height, measured, for example, as a percentage of total disc height. Can be further configured to restore power. Moreover, in certain embodiments, the same and/or similar techniques or steps may be similarly applied to define the distribution of horn formation in the cage and obtain a well-defined overall angle correction value.

Returning to FIG. 11A, in some embodiments, the system can be configured to access spinal measurement data for a healthy or asymptomatic population at block 1102. In some embodiments, the system includes data related to height and/or angulation of one or more particular discs as a percentage of total disc height and/or as a percentage of total horn formation. A database 1104 may be included that stores data collected from healthy and/or asymptomatic populations. Such a database 1104 can be internal or external to the system. Such a database 1104 may be constructed by the system, or may be installed and/or otherwise accessed by the system.

In particular embodiments, database 1104 may be updated continuously and/or periodically. In particular, in some embodiments, the system, at block 1106, allows individual disc height and/or angulation distributions to be made available, eg, new and/or updated healthy population spine data. Can be configured and determined as a percentage of total disc height and/or horn formation in a healthy population. In some embodiments, this process may be repeated continuously and/or periodically to update database 1104.

More specifically, in order to build a reference database of healthy and/or asymptomatic populations, the system measures and/or measures individual disc heights along a straight line or a curve intersecting the medial vertebrae. It can be configured to determine in other ways. Additionally and/or alternatively, in certain embodiments, the system can be configured to measure disc height along the anterior and/or posterior line. In some embodiments, the system can be configured to automatically measure or determine disc height based on medical images. In other embodiments, intervertebral disc height is determined manually.

Based on the determined disc height and/or angulation measurements, the system may calculate an average distribution of all disc heights and/or horn formations as a percentage of total disc height and/or horn formation. Can be configured. By doing so, the system can be configured to obtain a baseline percentage of disc height distribution for a healthy population, as shown in the schematic 1120 of FIG. 11B. Moreover, the same and/or similar techniques or steps can be applied as well to define the distribution of horn formation in the cage and obtain a well-defined overall angle correction value.

This reference data 1120 can then be compared to specific patient data and used to determine a recommended cage for that patient. In particular, returning to FIG. 11A, the system analyzes at block 1108 one or more detailed images of the patient, such as X-ray, MRI, CT, to analyze one or more disc heights and/or angles. The distribution of formations can be configured to determine the patient's total disc height and/or as a percentage of horn formation.

To do so, returning to schematic 1116 of FIG. 11B, the system determines the height of one or more individual discs of the patient along the medial plate curvature, eg, one of the patient's intended spinal segment. It can be configured to make a determination based on one or more medical images. In some embodiments, one or more disc heights for a particular patient can be measured or obtained preoperatively. In certain embodiments, the system can be configured to obtain patient lumbar disc height measurements automatically or manually from one or more medical images, level by level and/or as a total. Based on such measurements, the system may require cage replacement for one or more intervertebral discs if a particular patient has significant differences, for example, compared to healthy population data. Can be configured to determine what is possible. In the illustrated embodiment 1116, the system may determine that the patient may need to be cage replaced at the L4L5 and/or L5S1 positions.

As discussed, in certain embodiments, the system, at block 1108, uses the measured or determined individual disc heights and/or angulations to aggregate total disc heights for each disc height and/or angulation. It can be configured to calculate a percentage for the height and/or horn formation. For example, in the exemplary embodiment shown in FIG. 11B, and particularly in schematic 1118, the height of L4L5 and L5S1 as a percentage of total lumbar disc height can be determined to be approximately 9.5% and 19.0%. ..

In some embodiments, the system can be further configured at block 1110 to compare and/or analyze such percentages with data obtained from a healthy population. For example, in the exemplary embodiment shown in FIG. 11B, and particularly in schematic 1120, L4L5 and L5S1 heights as a percentage of total lumbar disc height are approximately 20.7% and 27.6% for a healthy population. Can be determined to be %.

If the calculated percentage of a particular disc height for a particular patient is too small compared to the value of a healthy population, the system will move the cage to restore an acceptable disc height for that disc. It can be configured to determine what needs to be embedded. In certain embodiments, if the particular disc height of a particular patient is below a predetermined threshold, as compared to a healthy population, the system may include implantation of a cage of a particular height or range of heights. Can be configured to recommend. For example, in some embodiments, the system may propose one or more cage heights, such as anterior and/or posterior cage heights, to replace a particular disc at block 1112 of FIG. 11A. Can be configured to.

In some embodiments, the system at block 1114 simulates one or more suggested cage height and/or horning results and/or one or more cage heights and/or Alternatively, it can be configured to compare simulated results in units of percentage of horn formation to data collected from healthy populations. Based on the results of such simulations, the system can be configured to generate modified suggested heights, angulations, and/or other dimensions for one or more cages. Thus, in some embodiments, the system can be further configured to compare and/or analyze such percentages against databases obtained from asymptomatic populations. Based on such a comparison, if the difference between such values is less than a predetermined threshold, the system can be configured to accept and complete the cage plan proposal. However, if the difference is greater than a certain predetermined threshold, the system rejects the cage planning proposal, for example increasing and/or reducing one or more heights, cornifications, or other dimensions of the cage. Can be configured to improve the proposal. Thus, in some embodiments, the system can be configured to apply an iterative process or technique. In some embodiments, one or more cage parameters thus determined, such as posterior height, anterior height, overall height, length, width, angulation, etc., are used in the system to, for example, , One or more patient-specific cages can be designed, made, and/or selected from an existing range or inventory.

Screw Planning As discussed herein, in certain embodiments, the system is configured to design, make, modify, and/or provide guidance for the selection of one or more patient-specific screws. it can. This is advantageous by reducing the inventory of screws that need to be manufactured and/or retained as stock, substantially reducing costs. The associated costs can be further reduced due to the reduced sterilization costs. An additional advantage is that fewer screws, cages, and/or sets thereof are provided as individualized caddies for each surgery, thus reducing surgery time and simplifying surgery for surgeons and staff. possible.

For example, unlike each single available screw or set of screws having a typical deformation tray of 400 or 500 or more, some embodiments have about 10 screws. , About 20 screws, about 30 screws, about 40 screws, about 50 screws, about 60 screws, about 70 screws, about 80 screws, about 90 screws, about 100 screws, about 110 screws, about 120 screws, about 130 screws, about 140 screws, about 150 screws, about 160 screws, about 170 screws, about 180 screws , About 190 screws, about 200 screws only, and/or, for example, for surgery that normally requires only about 2-60 screws, It is possible to provide only the number of screws in the range defined by two. In some embodiments, as a non-limiting example, for 5-level surgery, the system is selected and/or recommended to include up to 72 screws with specific dimensions in an individualized caddy. Can be configured as

Thus, in some embodiments, the system may analyze one or more medical images, such as two-dimensional radiographic images and/or MRI sagittal plane slices, and/or one from measurements obtained therefrom. Alternatively, multiple suitable dimensions can be evaluated and configured to design a patient-specific screw and/or other implant. In certain embodiments, the system combines one or more measurements obtained from one or more medical images with literature and/or data driven appendages to provide sufficient accuracy and/or precision. It can be configured to substantially determine a patient-specific screw design and/or one or more dimensions thereof.

FIG. 12A is a flow chart illustrating an exemplary embodiment of screw design, fabrication, modification, and/or selection. In particular, as shown in FIG. 12A, in some embodiments, the system can be used in certain vertebrae that can be used to design, make, modify, and/or select one or more patient-specific screws. It can be configured to design and/or determine one or more dimensions of a patient-specific screw for insertion.

In certain embodiments, the system is configured to access and/or obtain at block 1202, for example, one or more two-dimensional and/or three-dimensional medical images of a patient's spine. The one or more medical images may include one or more sagittal and/or frontal X-ray images, flexion and/or extension X-ray images, CT images, MRI images, and/or one or more other It may include medical images acquired using imaging techniques. Thus, in some embodiments, screw selection and/or design may be based on 3D image scan data such as CT scans. Based on the CT scan, the system can be configured to slice the vertebrae into which the screw is inserted and to determine each required precision measurement of the screw, such as screw length and/or diameter. However, to do so requires three-dimensional multi-section reconstruction (MPR), which can be time consuming. Also, a CT scan that is not a routine image may be required.

In some embodiments, patient-specific screw design, selection, and/or recommendation may be based on three-dimensional medical images and/or two-dimensional medical images, as described above in connection with FIG. For example, in some embodiments, the system can be configured to utilize one or more sagittal plane radiographs only to obtain a particular measurement. In certain embodiments, the system is configured to utilize the X-ray image and the front X-ray image to obtain the composite three-dimensional image described above to obtain a more accurate estimate of screw length and/or diameter. it can. In some embodiments, the system can be configured to utilize a full 3D image to obtain a particular measurement.

In certain embodiments, the system can be configured to utilize one or more two-dimensional x-ray and/or MRI image data. X-ray images are a routine task and are widely available. In some embodiments, the system is based on analysis of one or more two-dimensional radiographs and/or MRI slices and one or more precision specification values of one or more patient-specific screws and And/or can be configured to obtain dimensions. In certain embodiments, the system may provide one or more measurements obtained from one or more two-dimensional radiographic images and/or MRI slices to one or more from literature and/or other data. In combination with other measurements and/or other characteristic values, for example, to obtain high accuracy and/or precision, at least to substantially reduce the total number of screws that may be required for a particular surgery. ..

More specifically, in some embodiments, the system comprises one or more feature values derived from spinal anatomical data, scientific/medical literature, surgeon preference or other input, and/or other The data can be utilized to configure specific values and/or assumptions in the determination of one or more parameters or variables for designing a patient-specific screw. Returning to FIG. 12A, in some embodiments, the system, at block 1204, is based at least in part on inputs that may be stored in the scientific or medical literature, spinal anatomical data, and/or surgeon preferences or database 1206. Can be configured to determine and/or obtain one or more values and/or assumptions. The one or more values and/or hypotheses can be specific to each vertebra of interest. For example, in certain embodiments, the system includes a vertebral endplate into which a screw is inserted at an angle (α) between a vertebral axis and a pedicle axis on a cross-section (β) that may be level-specific. Angulation of the screw in the sagittal plane, the ratio between screw length and screw insertion axis length (y/x), and/or vertebral body width (VBW) and minimum pedicle width (W) Can be configured to obtain one or more values and/or assumptions regarding ratios that can be level-specific between.

FIG. 12B is a schematic diagram illustrating certain aspects of screw design, fabrication, modification, and/or selection. In particular, as shown in diagram 1214 of FIG. 12B, in some embodiments, the system may assume and/or obtain horn angulation α of the embedded screw relative to the end plate into which the screw is inserted. Can be configured. For example, in some embodiments, it can be assumed that the implanted screw will be parallel to the end plate based on scientific literature and/or spinal anatomical data. In other words, the angulation α of the screw in the sagittal plane can be assumed to be equal or substantially equal to the vertebral (and upper vertebral endplate) angulation under consideration. Thus, in certain embodiments, this angulation α can be assumed to be zero based on, for example, scientific literature and/or spinal anatomical data. In some embodiments, the system can be configured to assign any other angle value to the angulation α, for example, based on surgeon addiction and/or preference.

Further, as shown in schematic 1216, in certain embodiments, the system determines the angle β between the vertebral axis and the pedicle axis (in cross-section) at each level, eg, in the scientific literature and/or Or it can be configured to derive and/or make assumptions from spinal anatomical data. The angle β between the vertebral axis and the pedicle axis (in the cross-section) at each level can be level-specific. In some embodiments, the system can be configured to assign any other angle value to the angle β, eg, based on surgeon addiction and/or preference.

Similarly, as shown in schematic diagram 1218, in some embodiments, the system can determine the ratio (y/x) between screw length and screw insertion axis (SIA), for example, in the scientific literature and/or spine. It can be configured to obtain and/or hypothesize based on anatomical data. For example, in some embodiments, the system can be configured to assume 70% penetration of the vertebral body by the screw for optimal bone screw scaffolding, as can be suggested by the scientific literature. In certain embodiments, the SIA may be surgeon-specific and/or may depend on particular patient preferences and/or surgical goals. Surgeon preferences can also be taken into account for screw orientation in the transverse plane (convergent or divergent, consistent with the pedicle). Based on data-driven processes, scientific literature, and/or surgeon preference, the system may include about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 70% It can be configured to assume SIA to be within a range defined by 80%, about 90%, and/or two of the above values.

Further, as shown in the schematic 1220, in certain embodiments, the system determines the ratio of vertebral body width (VBW) to minimum pedicle width (W), eg, in the scientific literature and/or spinal anatomical data. Can be obtained and/or hypothesized. The ratio between VBW and W can be level specific. Thus, in certain embodiments, the system can be configured to obtain a VBW to W ratio for each level of vertebra. In some embodiments, the system can be configured to assign any other angle value to VBW, W, and/or a ratio thereof based on, for example, surgeon addiction and/or preference, and is surgeon-specific. Can be

Returning to FIG. 12A, in some embodiments, the system may be configured at block 1208 to measure one or more parameters from the one or more medical images. In particular, the system includes one or more medical images from one or more screw insertion axis (SIA) projected lengths and/or vertebral body widths (VBWs) on the sagittal plane (SIAp) for each vertebra of interest. ) Can be configured to measure. In particular, in certain embodiments, SIAp and/or VBW measurements can be obtained from sagittal radiographs and/or MRI slices for each vertebra of interest. In certain embodiments, the end plate length can be measured from a sagittal plane X-ray image. In some embodiments, the length from the pedicle entrance to the anterior wall of the vertebra can be obtained. In certain embodiments, both the endplate length and the length from the pedicle entrance to the anterior wall of the vertebra can be measured for each vertebra of interest.

In certain embodiments, based on such assumptions and data from the scientific literature and/or surgeon input, and further based on SIAp and/or VBW, the system determines the length of each patient-specific screw and/or It can be configured to determine and/or estimate the diameter. In particular, in some embodiments, the system may, based in part on the SIAp, VBW, the angle between the vertebral axis and the pedicle axis, and/or the screw penetration ratio, at block 1210, for each screw for each level of interest. Can be configured to determine the length of the. In certain embodiments, the system determines, at block 1212, the diameter of each screw for each level of interest based in part on the ratio of SIAp, VBW, and/or VBW to minimum pedicle width (W). Can be configured to In some embodiments, VBW/W ratios are about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90 from the literature. % And/or can be assumed to be within the range defined by two of the above values.

Thus, in some embodiments, a patient is specific in the desired screw diameter and/or length to obtain sufficient scaffolding for one or more vertebrae of interest and/or all vertebrae or a subset thereof. Can be decided against. Prior to surgery, screw design, recommendations, and/or the selection of a particular screw can be based on any one of the patient-specific feature values and/or analyzes described above. Screw design, recommendations, and/or choices may vary with bone quality, screw diameter, length and/or material. For example, in certain embodiments, the screw diameter can range from about 3.5 mm to about 10.5 mm. In some embodiments, one or more screw parameters thus determined, such as length, width, material, scaffold, etc., are used in the system, eg, from an existing range or inventory, one or Multiple patient-specific cages can be designed, made, and/or selected.

Intraoperative Tracking Sensors Generally speaking, certain intraoperative imaging such as fluoroscopy and/or CT scans can be used for intraoperative evaluation of spinal curvature and/or its correction. However, such processes typically provide only an instantaneous view/evaluation of spinal curvature. Thus, enabling live tracking of spinal curvature/angulation, providing substantial surgeon assistance, thereby further enabling the surgeon to make additional corrections to the spine when live control is needed. Can be advantageous. At the same time, certain live tracking devices, such as those based on optoelectronic passive sensors, may disrupt the surgeon's workflow because many additional steps may be required compared to normal surgery.

Accordingly, some embodiments described herein provide systems, devices, and methods that enable intraoperative monitoring. In particular, in certain embodiments, the system tracks surgeon movements in real-time, near real-time, and/or substantially real-time, adding only a small footprint to the surgical workflow, which can be tracked with pre-operative planning and further. It can be configured to compare.

In some embodiments, the system can enable a surgeon to manipulate a patient's spine and/or position and/or location of one or more sensors attached to one or more vertebrae. It may be possible to track one or more orientations. The one or more sensors attached to the one or more vertebrae can be configured to obtain tracking data related to one or more position and/or orientation of the vertebrae to which the sensor is attached. Thus, in certain embodiments, based on such tracking data and/or guidance data derived therefrom, one or more sensor readings are then determined to be optimal, desirable, and/or predetermined for spinal positioning. The surgeon can manipulate the patient's spine until it is shown to be in agreement with the plan.

In some embodiments, intraoperative imaging steps or techniques such as fluoroscopy and/or CT scanning can be used for intraoperative imaging. For example, in certain embodiments, intraoperative fluoroscopy can be used to assess the position of the screw with respect to the anatomical morphology to provide intraoperative tracking. In certain embodiments, one or more sensors can be used with one or more infrared cameras and/or electromagnetic detection. In some embodiments, the location and/or orientation of one or more sensors and/or bone can be identified by the use of active sensors. In certain embodiments, one or more passive sensors may also be used.

In some embodiments, the system utilizes the use of one or more active sensors to position and/or orient one or more pedicle screws, and then one or more pedicle screws. Can be configured to identify the location and/or orientation of one or more bones and/or vertebrae attached thereto. Thus, in certain embodiments, the system does not require any receiver to interpret the position, orientation, and/or angulation of one or more sensors on a common axis system. Configured to utilize a dynamic sensor, in other words, in some embodiments, a total intraoperative tracking system and/or device may provide signals for only one or more sensors and one or more sensors. It may include one or more computer devices or systems that process and display one or more measurements obtained therefrom.

In some embodiments, the term sensor as used herein may include a power source such as a battery, a wireless transmitter, and one or more active and/or passive sensors for real-time tracking. .. In certain embodiments, the one or more sensors include one or more accelerometers and/or one or more gyroscopes, such as in the case of 6 degrees of freedom (DOF) and/or 9 DOFs. One or more inertial measurement units can be obtained, including: In some embodiments, the system may include one or more active sensors configured to be inertial measurement units at 6 DOFs and/or 9 DOFs. In some embodiments where the system can be configured to utilize one or more passive sensors, visual tracking can be utilized to obtain intraoperative tracking in real time, near real time, and/or substantially real time. it can. In other embodiments where only active sensors are used, the system can be configured to not rely on visual tracking. Rather, the system may utilize wireless transmission of operational data for intraoperative tracking in real time, near real time, and/or substantially real time.

In certain embodiments, the system determines and/or determines the relative orientation and/or position of two or more sensors attached to the patient's spine, for example by interpreting independent sensor data and/or measuring spinal curvature. Or it can be configured to calculate. In particular, in some embodiments, the system can be configured to use the gravity vector as a common reference to interpret independent sensor data obtained from two or more sensors. In certain embodiments, it may be assumed or considered that two of the three axes of each central unit are planes that are parallel or substantially parallel to the defined angle to the sagittal plane of the patient on the operating table. it can. In other words, in certain embodiments, the position and/or orientation of more than one sensor is such that the position data for two of the three axes collected by each sensor is in the sagittal plane of the patient on the operating table. It can be configured to be or assumed to be parallel or substantially parallel planes. Thus, in some embodiments, proper positioning of the inertial unit can be mechanically obtained by the sensor/implant interface.

In some embodiments, one or more sensors are provided on all vertebrae, and/or on bone structures, for example, by one or more interfaces provided via one or more implants/screws. Can be attached directly. In certain embodiments, the one or more sensors can be attached to only a portion of the vertebra. Thus, in some embodiments, one or more sensors may be attached only to a subset of vertebrae capable of obtaining useful position and/or angle data of the spine. FIG. 13A is a schematic diagram illustrating an exemplary embodiment of intraoperative tracking. As shown in FIG. 13A, in some embodiments one or more sensors 1302 can be attached only to the particular vertebra, for example, in which the spinal rod 1304 is implanted. For example, in certain embodiments, one or more sensors may be attached to the S1, L1 and T4 vertebrae to assess L1-S1 lordosis and/or T4-T12 kyphosis.

In some embodiments where the one or more sensors are directly coupled and/or attached to the one or more screws, the system provides that the angulation of the screws in the sagittal plane is determined by the vertebra (or top plate). It can be configured to assume substantially equal angulation. In certain embodiments, intraoperative fluoroscopy can be used to assess the position of the screw with respect to anatomical structures such as the vertebral endplates in the sagittal plane as well as other planes.

13B-13G show exemplary embodiments of screws and/or sensors that can be used for intraoperative tracking. In some embodiments, one or more screws and/or other implants can be uniaxial and/or polyaxial. 13B-13E show a single screw exemplary embodiment, and FIGS. 13F and 13G show a multiple screw exemplary embodiment.

In certain embodiments in which one or more single screw is used, the system tracks the position and/or angle of all implanted screws, thereby determining the position of the vertebra based on the position of the screws. Can be configured to track. A single screw may include only one sensor 1302 based on the hypothesis that all screw movement is due to rigid body motion only. In certain embodiments, a single screw may include one or more sensors 1302.

In some embodiments, the polyaxial screw is, for example, where certain movements or movements are due to rigid body movements of the vertebra itself, or at least partially, or between different parts of the screw, such as inside and outside the vertebra. One or more sensors and/or two or more sensors 1302 may be included so that it can be determined completely for rigid body motion or non-rigid motion due to the motion of the. In some embodiments, the system can be configured to determine that a particular movement is a rigid body movement if there is a correlation between two or more sensor readings.

In some embodiments, the screw top and/or other implants can include one or more active and/or passive sensors. In certain embodiments, the top of the screw and/or other implants may also include a power source, such as a battery, and/or a wireless transmitter, and one or more active and/or passive sensors. In some embodiments, the apex can be later cut off during surgery and not implanted. The sensor 1302, or at least one or more portions thereof, can then be reused, discarded, and/or diverted for future use. For example, in the exemplary embodiment shown in FIGS. 13B-13E, the system includes one or more of a monoaxial screw, a polyaxial screw, and/or other implants, each including an attachment portion 1306 and a top portion 1308. Can be configured for use. The top 1308 may include a single sensor 1302 for a particular screw. Similarly, in the exemplary embodiment shown in FIGS. 13F and 13G, the system may utilize one or more of a polyaxial screw, and/or other implants, including a mounting portion 1306 and a top portion 1308, respectively. Can be configured to. As shown, in certain embodiments, the top 1308 may include more than one sensor 1302 for each particular screw.

In some embodiments, the intraoperative tracking system or device may require at least two or more screws attached to the vertebrae, each of the two or more screws including at least one sensor. In certain embodiments, the intraoperative tracking system or device includes sensors attached to at least 1, 2, 3, 4, 5, 6, 7, 8, 9, and/or 10 one or more vertebrae. Screws may be required. In some embodiments, the intraoperative tracking system or device may require a certain range of numbers of screws that include at least one sensor. The range of numbers of this sensor is defined by the two abovementioned values.

In certain embodiments, when a screw that includes at least one, two, three, four, and/or more sensors is attached to a vertebra, the system causes the one or more screws and thus the current position of the vertebra to be determined. Can be configured to obtain one or more sensor readings of orientation, orientation, and/or angle. Based on the readings from the one or more sensors and/or the guidance generated therefrom, the surgeon can further manipulate the patient's spine as needed. For example, in some embodiments, an intraoperative tracking system and/or device can be configured to provide updated tracking data and/or analysis therefrom on a continuous and/or periodic basis, whereby Until the surgeon indicates that the one or more sensor readings are optimal for one or more positioning and/or orientation of the spine and/or are consistent or substantially consistent with a predetermined plan. Manipulate the patient's spine.

In some embodiments, the system also directs the surgeon to tip the spine in a particular manner and/or orientation to reach and/or more closely track the predetermined plan. , Guidance, and/or suggestions. In some embodiments, the surgeon can implant the spinal rod with 2, 3, 4, and/or more screws once the optimal or desired spinal placement is achieved. In certain embodiments, after implantation of the rod, the top of the screw containing the one or more sensors can be cut off.

In certain embodiments, the one or more sensors are not provided as part of the screw, but rather one or more surgical tools that can ultimately be used to attach the screw to a vertebra. Including the sensor. For example, a screwdriver, nut driver, or other specialized or conventional surgical tool configured to attach a pedicle screw, scaffold, and/or other implants may have one or more actives for intraoperative tracking purposes. And/or passive sensors may be included. In certain embodiments, the intraoperative tracking system requires that at least 1, 2, 3, 4, 5, 6, 7, 8, 9, and/or 10 surgical tools include one or more sensors. Can be In certain embodiments, an intraoperative tracking system or device may require a certain range of tools to include at least one sensor, which range is defined by two above-mentioned values.

In some embodiments, a surgical tool that includes one or more sensors for intraoperative tracking purposes provides current position and/or orientation data of the surgical tool, for example, 6 DOFs and/or 9 DOFs. It may include buttons and other signaling mechanisms to measure and/or save. Thus, in certain embodiments, when a screw, scaffold, or other implant is placed in place, such as attached to a vertebra using such a surgical tool, the surgeon or other medical personnel may use the tool. A sensor therein can be enabled, thereby detecting and/or providing current orientation and/or position data. Thus, in some embodiments, the intraoperative tracking system can be configured to provide temporally frozen data rather than providing real-time tracking data.

14A-14E show exemplary embodiments of tools and/or sensors that can be used for intraoperative tracking. As shown in FIG. 14A, in some embodiments driver 1400 may include one or more sensors 1302 and/or other electronic components for intraoperative tracking. In certain embodiments, as shown in the exemplary embodiment of FIG. 14A, one or more sensors 1302 and/or other electronic components can be located in a driver shaft 1402. As shown in the exemplary embodiment of FIG. 14B, in some embodiments one or more sensors 1302 and/or other electronic components can be located in the handle 1404 of the driver. Further, as shown in the exemplary embodiment of FIG. 14C, nut driver 1406 can include one or more sensors 1302, eg, in shaft 1408 and/or handle.

As shown in the exemplary embodiments of FIGS. 14D and 14E, an additional housing structure 1410 configured to be coupled to a screw 1306, nuts, scaffolds, and/or other implants can be used to activate one or more active structures. And/or may include a passive sensor 1302. In certain embodiments, the additional housing structure 1410 may also include a power source, such as a battery, and/or a wireless transmitter, and one or more active and/or passive sensors 1302. In some embodiments, the additional housing structure 1410 can be configured to be used and/or coupled to the removable handle 1412. The additional housing structure 1410 can be attached to the screw 1306 and/or other pedicle scaffold or implant. While the spine is adjusted during surgery, the additional housing structure 1410 is still attached to the screw 1306 so that one or more sensors 1302 provide data related to the orientation and/or position of the screw 1306. Can be provided. After the spine has been adjusted to an acceptable level, the spinal rod 1304 can be inserted and screws 1306 and/or other pedicle scaffolds or implants can be secured to the vertebrae using the removable handle 1412 and then added. Housing structure 1410 can be removed. In some embodiments, the additional housing structure 1410 can provide additional space or volume for placement of one or more sensors, power supplies, and/or wireless transmitters.

Predictive Modeling In some embodiments, the system is configured to generate one or more predictive models or algorithms for surgery. In certain embodiments, one or more predictive models and/or algorithms are configured to predict one or more surgical parameters and/or variables that may result from, for example, spinal surgery. In some embodiments, the predictive model and/or algorithm is configured to generate a surgical plan to achieve the desired surgical outcome. For example, the system disclosed herein can be configured to access pre-operative patient input data and generate a surgical plan for implanting a spinal rod into a patient, and the surgical plan that is personalized and generated for the patient is , Configured to generate optimal postoperative spinal curvature for a particular patient.

When a patient undergoes surgery by a physician, the surgical outcome can usually be determined based on the surgeon's estimates and previous surgical experience. For example, if a spinal rod is implanted in a patient, the surgeon can analyze the patient's body and other features. Based on these observations, the surgeon can select a particular surgical parameter that provides a general estimate and/or believes the surgeon will have better spinal curvature post-operatively on the patient. However, in reality, surgeon estimates and selected surgical parameters may not provide the most desired or optimal surgical results.

For example, when performing spinal surgery to improve patient spinal curvature, the physician may select the spinal rod curvature to implant in the patient. The rod curvature selection can be determined and/or estimated by the surgeon based on the patient's physician's observations, and such determination and estimation can result in a patient having less than optimal spinal curvature after surgery. Therefore, a system that can predict postoperative surgical parameters based on preoperative patient characteristics may be beneficial to the surgeon and/or patient. For example, prior to performing spinal surgery, determine one or more optimal surgical parameters to be utilized in a surgical plan to achieve optimal postoperative spinal curvature for a particular patient with particular characteristics. That would be useful. Certain systems, methods, and apparatus disclosed herein are configured to address the aforementioned issues.

In particular, in some embodiments, the system can be configured to access pre-operative patient characteristics and input one or more such variables into the prediction algorithm. In certain embodiments, the system utilizes a predictive algorithm to generate one or more surgical plans having one or more specific surgical parameters predicted to produce optimal postoperative outcomes for the patient. Can be configured to For example, the system can be configured to receive one or more patient characteristics such as preoperative spinal curvature and angle, patient age, genetic map or status, and/or other variables. In particular, the presence of particular genes may be correlated with particular conditions such as scoliosis and/or surgical outcome. The system can be configured to utilize such patient characteristics and/or variables for input to the prediction algorithm. Based on the predictive algorithm, the system outputs specific surgical parameters such as optimal spinal rod curvature and/or instrumentation position and/or other variables to achieve optimal post-operative spinal curvature of the patient. Can be configured.

In some embodiments, the system can be configured to utilize one or more prediction algorithms to generate a prediction of post-operative outcome. For example, the system can be configured to access one or more patient characteristics as well as surgical parameters that the surgeon intends to use in surgical planning. In some embodiments, the system can be configured to utilize a predictive algorithm to determine post-operative results obtained from surgical parameters associated with the surgical plan. For example, the system can be configured to access patient characteristics such as preoperative spinal curvature and/or angle, patient age, genetic status, and/or other variables. The system can also be configured to access the curvature of the spinal rod that the surgeon intends to implant in the patient. In some embodiments, the system can be configured to generate a predictive post-operative spinal curvature of the patient based on input variables, in this example, patient characteristics and curvature of a spinal rod implanted in the patient.

Those skilled in the art will appreciate that the systems disclosed herein are applicable to myriad surgical procedures and are not intended to be limited to spinal surgery. For example, the systems disclosed herein are applicable to any type of surgery, including, but not limited to, orthopedic surgery such as cervical, head, hand, foot, leg, and arm surgery.

In some embodiments, the system is configured to generate a predictive model for predicting post-operative parameters. In some embodiments, the system can be configured to generate a predictive model by selecting a data set containing preoperative and/or postoperative data for one or more patients. As a non-limiting example, in some embodiments, the system is configured to identify all cases with PJK (proximal condylar kyphosis) and remove such cases from the dataset. .. In some embodiments, the system is configured to remove all pediatric cases from the dataset. In some embodiments, the removal of pediatric cases may be based on knowledge of previous cases in the dataset.

In some embodiments, the system is configured to divide the data into different groups based on the level of instrumentation. For example, the system divides the data set into a first group instrumented at locations L1-L5 and S1-Iliac and a second group instrumented at locations T10-T12 and S1-Iliac. Can be configured as For each group, in some embodiments, the system may divide the data into training and test sets (eg, about 75% data into training sets, about 25% data into test sets). Composed.

In some embodiments, the system includes one or more input parameters, such as age, PI preoperative value, PT preoperative value, LL preoperative value, TK preoperative value, SVA preoperative value, lower instrumentation. It is configured to select a level, an upper instrumentation level, an LL postoperative target value, a surgeon, a weight, a shape of a preoperative spline, a preoperative X-ray, and the like. In some embodiments, the system is configured to normalize the range of input parameters and/or utilize scaling methods.

In some embodiments, the system is configured to normalize the data based on the training set. In some embodiments, the system is configured to select the first model type from multiple model types such as, for example, linear model, neural network, deep learning, SVR, and the like. In some embodiments, the system is configured to use cross validation to select the best form. In some embodiments, the system is configured to perform cross validation by splitting the dataset into a new training set and a new test set. In some embodiments, the system is configured to train the model with the new training set and evaluate the results with the new test set.

In some embodiments, the system is configured to repeat the training process until each data is on the test set once, and only once. In some embodiments, the system is configured to train the model with the new training set and evaluate the results with the new test set. In some embodiments, the system is configured to utilize a linear model named Least Angular Regression (LARS) with a regularization and variable selection algorithm least abruption shrinkage and selection operator (LASSO). In some embodiments, the system is configured to test the trained model with a test set to determine if the trained model meets the accuracy threshold level. In some embodiments, the system utilizes a trained model to compare the proposed surgical plan to determine whether the surgical plan is optimal for the patient and/or the optimal surgical procedure for patients with certain patient characteristics. It is configured to determine whether to bring about a post-result.

FIG. 15 is a flow chart illustrating an exemplary embodiment of predictive modeling. In the exemplary embodiment shown, the system may be configured to access and/or retrieve one or more preoperative, intraoperative, and/or postoperative data sets at block 1502. The one or more data sets can access and/or retrieve one or more databases, such as the planning database 216 and/or the surgery database 218, among others.

In a particular embodiment, the system may be configured to determine whether the retrieved or accessed dataset includes post-operative data at block 1504. If the data set includes post-operative data, the system, at block 1506, sets one or more variables of interest, such as those described herein, to post-operative data and/or associated pre- and/or intra-operative data. It can be configured to identify from a dataset. Based, in part, on the identified one or more variables, the system can be configured at block 1508 to train the block prediction modeling algorithm according to one or more steps or techniques described herein. This training step and/or technique and/or portions thereof may be repeated as needed. For example, in certain embodiments, the system may train additional algorithms and/or portions thereof, as additional data from additional patients and/or additional post-operative data from known patients may be available. It is configured to iterate.

In some embodiments, if the retrieved or accessed data set is for a new case and thus does not contain post-operative data, the system may include one or more predictive modeling algorithms for such pre-operative input. It can be configured to apply to the data. In particular, in certain embodiments, the system may, at block 1510, identify one or more variables from the input preoperative data and/or compare it to one or more other data sets. Can be configured. In some embodiments, based on comparisons and/or other data analysis, the system can apply one or more predictive modeling algorithms to the input preoperative data. Thereafter, in some embodiments, the system configures at block 1512 to generate one or more predicted surgical outcomes and/or plans and/or one or more variables thereof based on the predictive model. it can. In certain embodiments, based at least in part on the resulting surgical plan and/or one or more of its variables, at block 1514, the system may include one or more spinal rods, cages, and/or screws. Or it can be configured to create, modify, select, and/or provide guidance for the selection of multiple spinal implants.

Other Embodiments for Predictive ModelingIn some embodiments, the system implements a computer-implemented method configured to generate a predictive model for determining post-operative parameters such as thoracic kyphosis or pelvic tilt. The computer-implemented method includes accessing an electronic database dataset, the dataset comprising patient-related data (eg, radiographic or clinical information) and surgical strategies (eg, upper instrumented vertebrae). , Lower instrumented vertebrae, etc.). In some embodiments, the computer-implemented method is configured to be defined in a dataset where the parameters must be inputs to the model and the parameters must be outputs from the model. For example, the output of the model can be a parameter that the system is configured to predict.

In some embodiments, the system is configured to optionally divide the dataset into multiple categories based on spinal surgery area knowledge, eg, the dataset is separated into adult and pediatric cases. Can be configured as In some embodiments, the system can be configured to generate a predictive model for each category. In some embodiments, the system is configured to divide the data into a first subcategory and a second subcategory, the first subcategory being used for training and the second subcategory being Used for testing predictive models. In some embodiments, the system is configured to normalize the data with the first category.

In some embodiments, the system is configured to select a model algorithm, eg, neural network, support vector regression, linear model, etc. In some embodiments, the system is configured to select a model based on using a cross validation strategy. In some embodiments, the system inputs one or more input values to the model based on the first subcategory and trains the statistical model based on the output values of the first subcategory. Is composed of. In some embodiments, the system inputs one or more input data values to the generated trained model and compares the output generated by the model with the output value of the first subcategory. Composed. In a particular embodiment, a model is generated based on the above comparisons, and the model's performance is known. In some embodiments, the system is configured to store the first trained statistical model in a data repository. In some embodiments, the system includes a computer processor and electronic storage. In certain embodiments, one or more of the above-identified steps or techniques are repeated for each category defined when the dataset is partitioned based on the spine surgery area knowledge blocks described above. ..

In some embodiments, the system is configured to perform a computer-implemented method for generating a predictive model for assessing post-operative parameters, the computer-implemented method accessing an electronic database dataset. , And the data set includes data collected from one or more patients and spinal surgery strategies adopted by the one or more patients. In some embodiments, the system is configured to classify the dataset into one or more categories based on spinal surgery area knowledge. In some embodiments, the system is configured to divide the data into a first subcategory and a second subcategory for each category, the first subcategory being used for training, Two subcategories are used for testing predictive models.

In some embodiments, the system is configured to normalize the data for the first subcategory. In some embodiments, the system is configured to select a model algorithm for the first subcategory of data. In some embodiments, the system inputs a first set of input values from the first subcategory into a model algorithm and trains a predictive model based on a first set of output values from the first subcategory. To be configured. In some embodiments, the system inputs a second set of input values from the second subcategory into the trained prediction model and trains the second set of output values from the second subcategory into the trained prediction model. Configured to compare the results generated by. In some embodiments, the system is configured to store the training predictive model in a data repository for implementation or future use. In some embodiments, the post-operative parameters include one or more of thoracic kyphosis or pelvic tilt. In some embodiments, the system includes a computer processor and electronic storage.

In some embodiments, the data collected from one or more patients comprises one or more x-ray or clinical information. In some embodiments, the surgical strategy employed for one or more patients includes one or more of data related to upper or lower instrumented vertebrae. In some embodiments, the spinal surgery area knowledge comprises one or more of adult or pediatric cases. In some embodiments, the model algorithm comprises one or more of a neural network, support vector regression, or a linear model. In some embodiments, the model algorithm is selected using a cross validation strategy.

In some embodiments, the system is configured to perform a computer-implemented method to generate a predictive model for estimating postoperative thoracic kyphosis and pelvic tilt parameters, the computer-implemented method comprising an electronic database. Of data from the spine surgery, the spine surgery including at least upper and lower instrumented vertebrae. In some embodiments, the system is configured to analyze the dataset and divide the dataset into multiple categories, the multiple categories including a first category containing data from surgery, and An instrumented vertebra is located between the L1 and L5 vertebrae and a lower instrumented vertebra is located between S1 and iliac.

In some embodiments, the system is configured to select a first category and access data from the surgery, the data comprising patient age, pelvic morphology for each of the first category of surgery. Anterior value, pelvic tilt preoperative value, lumbar lordotic preoperative value, thoracic kyphosis preoperative value, sagittal vertical axis preoperative value, lower instrumented vertebra value, upper instrumented vertebra value, or lumbar lordosis operation It includes one or more of the target values. In some embodiments, the system is configured to normalize the first category of data.

In some embodiments, the system is configured to divide the data into a first subcategory and a second subcategory, the first subcategory being used for training and the second subcategory being Used to test predictive models to determine postoperative thoracic kyphosis and pelvic tilt parameters. In some embodiments, the system is configured to input the preoperative data values in the first subcategory into the plurality of statistical models and train the statistical model based on the postoperative data values. In some embodiments, the system inputs pre-operative data values in the second sub-category into a plurality of trained statistical models and outputs values from the plurality of trained statistical models in a second sub-category of surgery. It is configured to be compared with the post data value.

In some embodiments, the system is configured to select a first trained statistical model from the plurality of trained statistical models, the first trained statistical model based on the comparison and post-operative data values. Produced the output value closest to. In some embodiments, the system is configured to store the first trained statistical model in electronic storage. In some embodiments, the system includes a computer processor and electronic storage.

In some embodiments, the system is configured to perform a computer-implemented method for generating a surgical plan based on a predictive model for assessing post-operative parameters, the computer-implemented method comprising: Accessing one or more medical images of a portion of the. In some embodiments, the system is configured to analyze one or more medical images to determine one or more preoperative variables associated with a patient's spine. The anterior variables are UIL, LIL, patient age, pelvic morphology preoperative value, pelvic tilt preoperative value, lumbar lordotic preoperative value, thoracic kyphosis preoperative value, or sagittal vertical axis preoperative value. At least one is included. In some embodiments, the system is configured to generate a prediction of one or more post-operative variables based at least in part on the application of the prediction model, the prediction model including one or more of the following: It is generated by the process of.

In some embodiments, the predictive model comprises access to a dataset of an electronic database, the dataset being adapted to data collected from one or more previous patients and to one or more previous patients. Including spinal surgery strategies. In some embodiments, the predictive model is configured to classify the dataset into one or more categories based on spinal surgery area knowledge. In some embodiments, the predictive model is configured to normalize the data for the first subcategory.

In some embodiments, the predictive model is configured to select a model algorithm for the first subcategory of data. In some embodiments, the predictive model inputs the first set of input values of the first subcategory to the model algorithm and trains the predictive model based on the first set of output values of the first subcategory. To be configured. In some embodiments, the predictive model inputs the second set of input values of the second subcategory into the trained predictive model and the predictive model trained on the second set of output values of the second subcategory Configured to compare the generated results.

In some embodiments, the post-operative parameters of the predictive model include one or more of thoracic kyphosis or pelvic tilt. In some embodiments, the system is configured to generate a surgical plan based at least in part on the one or more predicted post-operative variables generated by the predictive model. In some embodiments, the surgical plan includes at least one of the number of cages to implant, the location of implantation of the cage, the length of the spinal rod for implantation, or the curvature of the spinal rod. In some embodiments, the system includes a computer processor and electronic storage.

In some embodiments, the system is configured to perform a computer-implemented method to generate a surgical plan based on a predictive model for assessing postoperative thoracic kyphosis and pelvic tilt parameters. The method of implementation includes accessing one or more medical images of a portion of a patient's spine. In some embodiments, the system is configured to analyze one or more medical images to determine one or more preoperative variables associated with a patient's spine. The anterior variables are UIL, LIL, patient age, pelvic morphology preoperative value, pelvic tilt preoperative value, lumbar lordotic preoperative value, thoracic kyphosis preoperative value, or sagittal vertical axis preoperative value. At least one is included. In some embodiments, the system is configured to generate a prediction of one or more post-operative variables based at least in part on the application of the prediction model, the prediction model including one or more of the following: It is generated by the process of.

In some embodiments, the predictive model is configured to access a dataset of an electronic database, the dataset comprising data from spinal surgery, the spinal surgery comprising at least upper and lower instrumented vertebrae. including. In some embodiments, the predictive model is configured to analyze the dataset to divide the dataset into multiple categories, the multiple categories including a first category that includes data from surgery, The upper instrumented vertebra is located between the L1 and L5 vertebrae and the lower instrumented vertebra is located between S1 and iliac.

In some embodiments, the predictive model is configured to select a first category and access data from the surgery, the data comprising patient age, pelvic morphology for each of the first category of surgery. Preoperative value, pelvic tilt preoperative value, lumbar lordotic preoperative value, thoracic kyphosis preoperative value, sagittal vertical axis preoperative value, lower instrumented vertebra value, upper instrumented vertebra value, or lumbar lordosis It includes one or more of the post target values. In some embodiments, the predictive model is configured to normalize the first category of data.

In some embodiments, the predictive model is configured to partition the data into a first subcategory and a second subcategory, the first subcategory used for training, and the second subcategory is , Used to test predictive models to determine postoperative thoracic kyphosis and pelvic tilt parameters. In some embodiments, the predictive model is configured to input the preoperative data values in the first subcategory into the plurality of statistical models and train the statistical model based on the postoperative data values. In some embodiments, the predictive model inputs the preoperative data values in the second subcategory into the plurality of trained statistical models and outputs the output values from the plurality of trained statistical models in the second subcategory. Configured for comparison with post-operative data values.

In some embodiments, the predictive model is configured to select a first trained statistical model from the plurality of trained statistical models, the first trained statistical model based on the comparison and post-operative data. Generated the output value closest to the value. In some embodiments, the one or more predicted post-operative variables is at least one of a lumbar lordosis postoperative value, a thoracic kyphosis postoperative value, or a sagittal vertical axis postoperative value. including. In some embodiments, the system is configured to generate a surgical plan based at least in part on the one or more predicted post-operative variables. In some embodiments, the surgical plan includes at least one of the number of cages to implant, the location of implantation of the cage, the length of the spinal rod for implantation, or the curvature of the spinal rod. In some embodiments, the system includes a computer processor and electronic storage.

Data Elements/Parameters for Predictive Modeling In some embodiments, a system can perform one or more steps or techniques associated with predictive modeling that can be collected from one or more patients. It may be configured to receive, access and/or obtain a plurality of the following data elements or parameters.

In particular, in certain embodiments, the system may, for example, age at surgery, gender, height, weight, activity level, anesthesia date, disability, education, home care requirements, insurance coverage, work, race, work/school. /Configured to receive, access, and/or obtain one or more demographic characteristics such as the day back to sport, socioeconomic status, etc.

In some embodiments, the system provides one or more patient-reported results, such as the Oswestry Low Back Disorder Questionnaire (ODI), Cervical Disability Index (NDI), Scoliosis Study Group ( SRS-22) can be configured to receive, access, and/or obtain a questionnaire, Nurick, etc.

In certain embodiments, the system may be, for example, T4-T12 TK (chest lordosis), L1-S1 LL (=lumbar lordosis), lateral C7-sacral SVA (=sagittal vertical axis), PT (=pelvis). Receive, access, and/or one or more radiographic parameters such as preoperative and/or postoperative data such as tilt), PI (pelvic morphology), L1-S1 TK (= kyphosis) /Or can be configured to obtain this.

In some embodiments, the system comprises a vertebral deviation ThL/lumbar flexion-CSVL, C2T1 pelvic angle (=CTPA,°), C2C7 SVA (mm) (=sagittal vertical axis), cervical lordosis, Lenke classification, proximal adjacent intervertebral kyphosis (PJK), Rod Tracing, SS, T1 slope (T1S,°) T1 tilt angle and direction, T10-L2, T12-S1 lumbar lordosis, T2-T12, T2- T5, T5-T12 thoracic kyphosis, Th apical vertebra, Th flexion, Th curve, Th curve level, (Th/L lumbar vertebrae, Th/L lumbar curve, Th/L lumbar curve curve direction, Th/L lumbar curve Curvature level), T1 pelvic angle (TPA), anatomical kyphosis, anatomical lordosis, Cobb angle, coordinates of all vertebral corners and femoral heads in sagittal and coronal planes, apical deviation Th Precursor-C7 Plum, Deflection of the Vertebral Vertebra Pre- or post-operative data such as Th Curvature-CSVL, Computed tomography, Intervertebral disc angulation below the instrumented vertebral space (EIV), EIV angulation, Coronary C7 To CSVL, T1 S-CL (°), TH CUrve-curvature direction, Y-shaped cartilage, Th upper flexion, Th upper curl, Th upper curl-curvature direction, Th upper curl-level, ear canal, pelvic tilt, One or more other radiographic parameters such as pelvic rotation, acetabular index, etc. can be similarly configured to receive, access, and/or obtain.

In some embodiments, the systems disclosed herein include instrumentation material, instrumentation size, instrumentation type, bottom instrumentation, MIS (=minimally invasive surgery), number of levels, osteotomy performed. , Rod curvature and/or angle, rod cutting parameters, top instrumentation parameters, upper instrumented vertebrae (UIV), lower instrumented vertebrae (LIV), surgeons, surgical techniques (in some embodiments, surgeon's surgery 1) such as X-ray examination as images, scanners, MRI images (images or image sets), using machine learning algorithms to analyze that the technology can simulate surgery and rods that meet surgeon expectations It can be configured to generate a spinal surgery strategy that includes one or more surgical data parameters.

In an exemplary embodiment, the first set of input values for preoperative and/or postoperative data includes: T4-T12 TK (= thoracic lordosis), L1-S1 LL (= lumbar lordosis). ), lateral C7 to sacrum (SVA) (= sagittal vertical axis), lowest instrumented vertebra (LIV), top instrumented vertebra (UIV), pelvic tilt, age at surgery, and pelvic morphology (PI).

In an exemplary embodiment, the first set of output values for pre-operative and/or post-operative data includes: T4-T12 TK (= thoracic lordosis), L1-S1 LL (= lumbar lordosis). ), and pelvic tilt.

System FIG. 16 is a schematic diagram illustrating an embodiment of a system for developing patient-specific spinal therapy, surgery, and procedures. In some embodiments, the main server system 1602 includes an image analysis module 1604, a case simulation module 1606, an intraoperative tracking module 1608, a data usage module 1610, a predictive modeling module 1628, a planning database 1612, a surgery database 1614, a surgeon database 1616, And/or may consist of a literature database 1618. The main server system can connect to the network 1620. The network may connect the main server to one or more of implant fabrication facility system 1626, one or more healthcare facility client systems 1622, and/or one or more user access point systems 1624.

Image analysis module 1604 may function by providing image analysis and/or associated functionality described herein. The case simulation module 1606 may function by performing surgical planning, case simulation, and/or performing related functions described herein. The intra-operative tracking module 1608 may function by performing intra-operative tracking and/or performing the associated functions described herein. The data utilization module 1610 may function by retrieving data from and/or storing data in one or more databases and/or related functions. Predictive modeling module 1628 may function by performing one or more predictive modeling steps described herein.

The plan database 1612 may provide a collection of all plan and/or related data generated by the system. Surgery database 1614 may provide a collection of all surgical procedures performed using the system and/or related data. Surgeon database 1616 may provide a collection of all surgeons utilizing the system and/or relevant data such as surgeon preferences, skill levels, and the like. Literature database 1618 may provide a collection of scientific literature related to spinal surgery.

Computer Systems In some embodiments, the systems, processes, and methods described herein are implemented with a computing system, such as that shown in FIG. Exemplary computer system 1702 is in communication with one or more computing systems 1720 and/or one or more data sources 1722 via one or more networks 1718. FIG. 17 illustrates one embodiment of computing system 1702, but the functionality provided by the components and modules of computer 1702 may be integrated into fewer components and modules, or may be subdivided into additional components and modules. It will be understood.

Computer system 1702 may include a patient-specific spinal therapy, surgery, and procedural module 1714 that performs the functions, methods, acts, and/or steps described herein. The patient-specific spinal therapy, surgery, and procedural module 1714 is performed on the computer system 1702 by the central processing unit 1706, which is discussed further below.

In general, as used herein, the word "module" means logic having an entry point and an exit point implemented in hardware or firmware or for a collection of software instructions. The module is described in a programming language such as JAVA (registered trademark), C or C++, PYPHON. Software modules may be compiled or linked into an executable program, installed in a dynamic link library, or written in an interpreted language such as BASIC, PERL, LUA, or Python. Software modules may be called from other modules or themselves and/or may be called in response to a detection event or interrupt. Hardware-implemented modules include concatenated logic units such as gates and flip-flops, and/or may include programmable units such as programmable gate arrays or processors.

Generally, modules described herein refer to logical modules that may be combined with other modules or divided into sub-modules regardless of physical configuration or storage. Modules may be executed by one or more computing systems and stored on or in any suitable computer-readable medium, or wholly or partially in specifically designed hardware or firmware. Can be implemented in. Although not all calculations, analyzes, and/or optimizations require a computer system, some of the methods, calculations, steps, or analyzes described above can be facilitated by the use of a computer. Moreover, in some embodiments, the process blocks described herein may be altered, rearranged, combined, and/or omitted.

Computer system 1702 includes one or more processing units (CPU) 1706, which may include a microprocessor. Computer system 1702 includes physical storage device 1710, such as random access memory (RAM), for temporary storage of information, read only memory (ROM) for permanent storage of information, and auxiliary storage, hard drive. , Mass storage device 1704, such as a rotating magnetic disk, solid state disk (SSD), flash memory, phase change memory (PCM), 3D crosspoint memory, diskette, or optical media storage device. Alternatively, the mass storage device may be implemented in a server array. The components of computer system 1702 are typically connected to a computer using a standard bus system. The bus system may be implemented using various protocols such as Peripheral Component Interconnect Standard (PCI), Micro Channel, SCSI, Industrial Standard Architecture (ISA) and Enhanced ISA (EISA) architecture.

Computer system 1702 includes one or more input/output (I/O) devices and interfaces 1712 such as a keyboard, mouse, touchpad, and printing press. The I/O device and interface 1712 may include one or more display devices that allow visual presentation of data to a user, such as a monitor. More specifically, the display device provides a presentation of the GUI as application software data and, for example, as a multimedia presentation. I/O device and interface 1712 also provides a communication interface to various external devices. Computer system 1702 may include, for example, one or more multimedia devices 1708 such as speakers, video cards, graphic accelerators, and microphones.

Computer system 1702 may run on various computing devices such as servers, Windows servers, structured query language servers, Unix servers, personal computers, laptop computers, and so on. In other embodiments, the computer system 1702 is suitable for controlling and/or communicating with cluster computer systems, mainframe computer systems and/or other large databases, performing high volume transaction processing and reporting from large databases. May be run on a computing system that produces The computing system 1702 is typically an operating system such as z/OS, Windows, Linux®, Unix, BSD, SunOS, Solaris, MacOS, or other compatible operating system, including operating systems under intellectual property. Controlled and coordinated by software. The operating system, among other things, controls and schedules computer processes for execution, performs storage management, provides file systems, networking, and I/O services, and provides a user interface such as a graphical user interface (GUI). To do.

The computer system 1702 shown in FIG. 17 is connected to a network 1718 such as LAN, WAN, or the Internet via a communication link 1716 (wired, wireless, or a combination thereof). Network 1718 communicates with various computing devices and/or other electronic equipment. Network 1718 is in communication with one or more computing systems 1720 and one or more data sources 1722. The patient-specific spinal therapy, surgery, and procedural module 1714 may be or may be accessed by the computing system 1720 and/or data source 1722 via an internet-enabled user access point. Connections can be direct physical connections, virtual connections, and other types of connections. Internet-enabled user access points may include a browser module that presents data using text, graphics, audio, video, and other media and allows interaction with the data via network 1718. ..

Accessing the computer system 1702 by the computing system 1720 to the patient-specific spinal therapy, surgery, and procedural modules 1714 is performed by the computing system 1720 or a data source 1722 personal computer, mobile phone, smartphone, laptop, tablet computer, e-book. It may be via an Internet-enabled user access point such as a terminal device, audio player, or other device capable of connecting to the network 1718. Such devices have browser modules implemented as modules that use text, graphics, audio, video, and other media to present data and interact with the data via network 1718. May enable.

The output module may be implemented as a combination of all-point addressable displays, such as a cathode ray tube (CRT), liquid crystal display (LCD), plasma display, or other type and/or combination of displays. Output modules may be implemented for communicating with input device 1712, which also stylize menus, windows, dialog boxes, toolbars, and controls (eg, radio buttons, check boxes, sliding scales, etc.). It also includes software with a suitable interface that allows user data access through the use of screen elements. Further, the output module is in communication with the set of input and output devices and receives signals from the user.

The input device may include a keyboard, rollerball, pen and stylus, mouse, trackball, voice recognition system, or pre-specified switch or button. Output devices may include speakers, display screens, printers, or voice synthesizers. Further, the touch screen may function as a hybrid input/output device. In another embodiment, the user may interact more directly without communicating over the Internet, WAN, or LAN, or similar network, such as via a system terminal connected to the score generator.

In some embodiments, the system 1702 was established between a remote microprocessor and a mainframe host computer for specific purposes such as uploading, downloading, interactive data in real time and online browsing of databases. It may include physical or logical connections. The remote microprocessor may operate computer system 1702, may be operated by an entity including a client server system or a main server system, and/or one or more data sources 1722 and/or one or more computing systems 1720. Can be operated by. In some embodiments, terminal emulation software may be used on the microprocessor to participate in the micro-mainframe link.

In some embodiments, the computing system 1720, which is internal to the entity operating the computer system 1702, is internally to the patient-specific spinal therapy, surgery, and procedural module 1714, an application executed by the CPU 1706, or It can be accessed as a process.

Computing system 1702 may include one or more internal and/or external data sources (eg, data source 1722). In some embodiments, the data repository and one or more of the above data sources include DB2, Sybase, Oracle, Codebase, and Microsoft SQL Server and other types of databases, such as flat file databases, entity-related databases, And object-oriented databases, and/or relational databases such as record-based databases.

Computer system 1702 may also access one or more databases 1722. Database 1722 may be stored in the database or in a data repository. Computer system 1702 may access one or more databases 1722 via network 1718 or directly to databases or data repositories via I/O devices and interfaces 1712. A data repository that stores one or more databases 1722 may reside within computer system 1702.

In some embodiments, one or more system features, methods, and apparatus described herein include URLs and/or cookies, such as for storing and/or transmitting data or user information. Is available. The uniform resource locator (URL) may include references to web addresses and/or web resources stored on a database and/or server. The URL may specify the location of the resource on the computer and/or computer network. The URL may include a mechanism for retrieving network resources. The source of the network resource can retrieve the URL, locate the web resource, and send the web resource back to the requestor. The URL can be converted to an IP address and the Domain Name System (DNS) can look up the URL and its corresponding IP address. URLs can be web pages, file transfers, emails, database access, and queries to other applications. The URL may include a series of characters such as path, domain name, file extension, host name, query, fragment, scheme, protocol identifier, port number, user name, flag, object, resource name, and so on. The systems disclosed herein can generate, receive, send, apply, parse, serialize, render, and/or perform the effects of URLs.

Cookies, also known as HTTP cookies, web cookies, internet cookies, and browser cookies, may include data sent from websites and/or data stored on your computer. This data may be stored by the user's web browser while the user is browsing. Cookies are useful for websites to store previous browsing information, such as shopping carts on online stores, button clicks, login information, and/or records of previously visited web pages or network resources. Information may be included. Cookies may also include user-entered names, addresses, passwords, credit card information, etc., for example. Cookies can also perform computer functions. For example, the authentication cookie can be used by an application (eg, a web browser) to identify whether the user has already logged in (eg, on a website). Cookie data can be encrypted to provide consumer security. Tracking cookies can be used to compile an individual's past browsing history. The system disclosed herein can generate and use cookies to access personal data. The system can also generate and use JSON web tokens to store trust information, HTTP authentication as an authentication protocol, IP address for tracking session or identity information, URLs.

Although the embodiments discussed herein relate to patient-specific spinal therapy, surgery, and procedures, the systems, methods, and devices disclosed herein provide any non-vertebral patient-specific therapy, surgery, and It can be used for procedures as well. Also, the systems, methods, and devices disclosed herein can be used with X-ray, MRI, CT, or any other imaging system or device that produces two-dimensional and/or three-dimensional medical image or video data. ..

Although the present invention has been disclosed in light of particular embodiments and examples, those of ordinary skill in the art will appreciate that other alternative embodiments and/or uses of the present invention beyond those specifically disclosed. , And obvious variations and equivalents thereof. In addition, although some variations of embodiments of the invention have been shown and described in detail, other variations that are within the scope of the invention will be readily apparent to those of ordinary skill in the art based on this disclosure. Will be. Also, various combinations or subcombinations of the particular features and aspects of some embodiments may be made, which are also intended to be within the scope of the present invention. Various features and aspects of the disclosed embodiments can be combined or substituted with each other to form various aspects of the disclosed invention's embodiments. None of the methods disclosed herein need be performed in the order listed. Therefore, it is intended that the scope of the invention herein disclosed should not be limited by the particular disclosed embodiments described above.

In particular, conditional terms such as "can", "could", "might", "may" etc. are used unless otherwise stated or used. It is generally intended to convey that particular embodiments include features, elements and/or steps, while other embodiments do not, unless the context clearly dictates otherwise. Accordingly, such conditional phrases typically require that features, elements and/or steps are somehow required for one or more embodiments, or that one or more embodiments may not include user input or prompts. Meaning that these features, elements, and/or steps, whether or not present, are necessarily meant to include logic to determine whether they are included in a particular embodiment or should be performed. Not intended. The headings used herein are for the convenience of the reader only and are not intended to limit the invention or the claims.

Furthermore, the methods and devices described herein are susceptible to various modifications and alternatives, specific examples of which are shown in the drawings and are described in detail herein. However, the invention is not limited to the particular forms or methods disclosed, but on the contrary, the invention is within the spirit and scope of various embodiments as set forth in the appended claims. It is to be understood that it includes all modifications, equivalents, and alternatives. In addition, the disclosure herein of any particular feature, aspect, method, property, characteristic, quality, attribute, element, etc. relating to an implementation or embodiment is used to It can be used in embodiments or embodiments. None of the methods disclosed herein need be performed in the order listed. The methods disclosed herein may include certain actions taken by a practitioner. However, the method may also include, expressly or implicitly, any third party guidance for these actions. The ranges disclosed herein also include any and all overlaps, subranges, and combinations thereof. The words "maximum", "at least", "greater than", "less than", "between" and the like include the stated numerical values. Numerical values preceded by terms such as "about" and "approximately" include the stated numerical value and must be construed accordingly (eg, as reasonably and accurately as possible). , ±5%, ±10%, ±15%, etc.). For example, "about 3.5 mm" includes "3.5 mm". Phrases preceded by a term such as “substantially” include the recited phrase and must be interpreted contextually (eg, as reasonably as possible under the circumstances). For example, “substantially constant” includes “constant”. Unless otherwise specified, all measurements are for standard conditions including temperature and pressure.

As used herein, a phrase that means a list of "at least one" item means any combination of those items that includes a single member. By way of example, "at least one of A, B, or C" includes A and B and C; A and B; A and C; B and C; and A; B; C. Conjunctive phrases such as phrases such as "at least one of X, Y, Z" are generally used to convey that an item, term, etc. can be at least one of X, Y, or Z, unless otherwise specified. Understood differently in the context in which it is used. As such, such connective phrases are generally not meant to mean that a particular embodiment requires at least one X, at least one Y, and at least one Z for each to be present.

Claims (20)

A system for developing one or more patient-specific spinal implants, comprising:
One or more computer-readable storage devices configured to store computer-executable instructions; and instructions executable by the computer-readable storage devices in communication with the one or more computer-readable storage devices. To the system,
Accessing one or more medical images of the patient's spine;
Based on one or more medical images,
Identifying one or more reference points along the spinal segment of interest; and identifying one or more portions of the one or more medical images around the identified one or more reference points. Rotating to obtain the desired surgical output curvature of the patient's spine,
Simulating the implantation of a spinal rod in the spinal segment of interest, further comprising:
Determining one or more dimensions, including diameter and curvature, of the patient-specific spinal rod for the spinal segment of interest based at least in part on a simulation of implantation of the spinal rod;
A spinal rod manufacture for making or selecting the patient-specific spinal rod for the spinal segment of interest, based at least in part on the determined size of implantation of the one or more patient-specific spinal rods. Or generating spinal rod manufacturing or data selection instructions for use in a selection device;
Determining the anterior longitudinal ligament length of the spinal segment of interest and the posterior longitudinal ligament length of the spinal segment of interest from the one or more medical images;
Determining from the one or more medical images the anterior curve length of the spinal segment of interest and the posterior curve length of the spinal segment of interest;
Based on the one or more medical images,
Increasing the posterior height of each of the one or more cages to a length in which the posterior flexure substantially matches or does not exceed the length of the posterior longitudinal ligament; and the anterior longitudinal Increasing the lordosis of each of the one or more cages while maintaining an anterior curve length shorter than the directional ligament,
Simulating the implantation of one or more cages into one or more intervertebral spaces within the spinal segment of interest further comprising:
One including a posterior height and an anterior height of each of the one or more patient-specific cages for the spinal segment of interest based at least in part on a simulation of implantation of the one or more cages. Or determining multiple dimensions;
One or more patient-specific cages for the one or more intervertebral spaces based at least in part on the determined one or more dimensions of each of the one or more patient-specific cages Generating a cage manufacturing or data selection instruction for use by a cage manufacturing or selecting device for manufacturing or selecting
Including one or more hardware computer processors configured to perform
The patient-specific spinal rod for the spinal segment of interest is made or selected from a range of existing spinal rods based at least in part on the generated spinal rod manufacturing or data selection instructions, the one or more The system, wherein one or more patient-specific cages for the intervertebral space are created or selected from an existing range of cages based at least in part on the generated cage manufacturing or data selection instructions. The system is
Determining a screw insertion axis projection length on the sagittal plane and a vertebral body width for each vertebra in the spinal segment of interest from the one or more medical images;
Implantation for each vertebra in the spinal segment of interest based on a given spinal anatomical data, literature, or surgeon preference, at least in part, relative to an end plate configured to attach the screw. Assumed or prescribed angulation of the screw, assumed or prescribed angle between vertebral axis and pedicle axis on cross-section, assumed or prescribed ratio between screw length and screw insertion axis length, and vertebral body width and vertebra Determining an assumed or predetermined ratio between pedicle width;
The determined screw insertion axis projected length on the sagittal plane, the determined vertebral body width, an assumed or predetermined angle between the vertebral axis and the pedicle axis on the cross section, and the screw length and screw insertion. One or more desired one or more patient-specific screws for insertion into each vertebra in the spinal segment of interest based at least in part on an assumed or predetermined ratio between axial lengths. Generate a length of;
Based on, at least in part, a determined screw insertion axis projection length on the sagittal plane, the determined vertebral body width, and an assumed or predetermined ratio between the vertebral body width and the pedicle width. Creating one or more desired diameters of one or more patient-specific screws for insertion into each vertebra within a spinal segment of interest; and said determined one or more desired lengths and Screw manufacturing or data selection for use by a screw manufacturing or selecting device for making or selecting said one or more patient-specific screws from an existing range of screws based at least in part on a desired diameter. Generating instructions,
The system of claim 0, further comprising: At least one of the one or more patient-specific screws includes one or more sensors, the one or more sensors configured to obtain intraoperative follow-up data, wherein the intra-operative follow-up data is The system of claim 0, including substantially real-time orientation and position data of a portion of the patient's spine, the intraoperative tracking data being configured to assist surgery. The one or more patient-specific screws are configured to be inserted into one or more vertebrae with a surgical tool, the surgical tool including one or more sensors; Or a plurality of sensors configured to obtain intraoperative tracking data, the intraoperative tracking data including substantially real-time orientation and position data of a portion of the patient's spine, wherein the intraoperative tracking data is a surgical procedure. The system of claim 0, configured to support the. A desired surgical output curvature of the spine is determined based at least in part on one or more predicted post-operative parameters,
The system is
UIL, LIL, patient age, pelvic morphology preoperative value, pelvic tilt preoperative value, lumbar lordotic preoperative value, thoracic kyphosis preoperative value, or sagittal plane by analyzing one or more medical images Determining one or more preoperative variables associated with the patient's spine, including at least one of the vertical axis preoperative values; and one or more procedures based at least in part on the application of the predictive model. Generating predictive values for post-variables, wherein the predictive model is
Accessing a data set of an electronic database containing data collected from one or more previous patients and spinal surgery strategies adopted by said one or more previous patients;
Grouping the dataset into one or more categories based on spinal surgery area knowledge;
Normalizing the data into a first subcategory;
Selecting a model algorithm for the data in the first subcategory;
Inputting a first set of input values from the first subcategory into the model algorithm to train a predictive model based on a first set of output values from the first subcategory;
Inputting a second set of input values from the second subcategory into the trained prediction model and comparing the results produced by the trained prediction model with the second set of output values from the second subcategory; And storing the trained predictive model for implementation,
Generate a predicted value, generated by
Further generating predictive values for one or more post-operative parameters by
The system of claim 0, wherein the post-operative parameters include one or more of pelvic tilt, lordosis, lordosis, or sagittal vertical axis. The medical image of the one or more vertebrae includes one or more of a sagittal plane radiographic image, a frontal radiographic image, a flexed radiographic image, a stretched radiographic image, or an MRI image. The system described in. The one or more medical images include one or more two-dimensional X-ray images, the system calibrates the one or more two-dimensional X-ray images, and the one or more two-dimensional X-ray images The system of claim 0, further causing generating a three-dimensional composite image based on the x-ray image. A system for developing one or more patient-specific spinal implants, comprising:
One or more computer-readable storage devices configured to store computer-executable instructions; and instructions executable by the computer-readable storage devices in communication with the one or more computer-readable storage devices. To the system,
Accessing one or more medical images of the patient's spine;
Determining from the one or more medical images the anterior longitudinal ligament length of the spinal segment of interest and the posterior longitudinal ligament length of the spinal segment of interest;
Determining from the one or more medical images the anterior curve length of the spinal segment of interest and the posterior curve length of the spinal segment of interest;
Based on the one or more medical images,
Increasing the posterior height of each of the one or more cages to a length in which the posterior flexure substantially matches or does not exceed the length of the posterior longitudinal ligament; and the anterior longitudinal Increasing the lordosis of each of the one or more cages while maintaining an anterior curve length shorter than the directional ligament,
Simulating the implantation of one or more cages into one or more intervertebral spaces within the spinal segment of interest further comprising:
One including a posterior height and an anterior height of each of the one or more patient-specific cages for the spinal segment of interest based at least in part on a simulation of implantation of the one or more cages. Or determining multiple dimensions;
Of the existing range based at least in part on the cage manufacturing or data selection instructions generated, based at least in part on the determined one or more dimensions of each of the one or more patient-specific cages. Cage making or data for use by a cage making or selecting device for making or selecting one or more patient-specific cages for said one or more intervertebral space made or selected from a cage Generating a selection instruction,
Determining a screw insertion axis projection length on the sagittal plane and a vertebral body width for each vertebra in the spinal segment of interest from the one or more medical images;
Implantation for each vertebra in the spinal segment of interest based on a given spinal anatomical data, literature, or surgeon preference, at least in part, relative to an end plate configured to attach the screw. Assumed or prescribed angulation of the screw, assumed or prescribed angle between vertebral axis and pedicle axis on cross-section, assumed or prescribed ratio between screw length and screw insertion axis length, and vertebral body width and vertebra Determining an assumed or predetermined ratio between pedicle width;
The determined screw insertion axis projected length on the sagittal plane, the determined vertebral body width, an assumed or predetermined angle between the vertebral axis and the pedicle axis on the cross section, and the screw length and screw insertion. One or more desired one or more patient-specific screws for insertion into each vertebra in the spinal segment of interest based at least in part on an assumed or predetermined ratio between axial lengths. Generate a length of;
Based on, at least in part, a determined screw insertion axis projection length on the sagittal plane, the determined vertebral body width, and an assumed or predetermined ratio between the vertebral body width and the pedicle width. Creating one or more desired diameters of the one or more patient-specific screws for insertion into each vertebra in a spinal segment of interest; and the determined one or more desired lengths And screw manufacturing or data for use by a screw manufacturing or selecting device for making or selecting said one or more patient-specific screws from an existing range of screws based at least in part on a desired diameter. Generating a selection command,
A system comprising one or more hardware computer processors configured to perform. At least one of the one or more patient-specific screws includes one or more sensors, the one or more sensors configured to obtain intraoperative follow-up data, wherein the intra-operative follow-up data is 9. The system of claim 8, including substantially real-time orientation and position data of a portion of the patient's spine, the intraoperative tracking data being configured to assist surgery. The one or more patient-specific screws are configured to be inserted into one or more vertebrae with a surgical tool, the surgical tool including one or more sensors; Or a plurality of sensors configured to obtain intraoperative tracking data, the intraoperative tracking data including substantially real-time orientation and position data of a portion of the patient's spine, wherein the intraoperative tracking data is a surgical procedure. 9. The system of claim 8, configured to support the. The system is
Based on the one or more medical images,
Identifying one or more reference points along the spinal segment of interest; and identifying one or more portions of the one or more medical images around the identified one or more reference points. Rotating to obtain the desired surgical output curvature of the patient's spine,
Simulating the implantation of a spinal rod into the spinal segment of interest further comprising:
Determining one or more dimensions, including diameter and curvature, of the patient-specific spinal rod for the spinal segment of interest based at least in part on a simulation of implantation of the spinal rod;
Use in a spinal rod manufacturing or selection device for making or selecting a patient-specific spinal rod for a spinal segment of interest based at least in part on the determined dimensions of the one or more patient-specific spinal rods. Generating spinal rod manufacturing or data selection instructions for,
Is further implemented,
9. The patient-specific spinal rod for the spinal segment of interest is made or selected from a range of existing spinal rods based at least in part on the generated spinal rod manufacturing or data selection instructions. system. A desired surgical output curvature of the spine is determined based at least in part on one or more predicted post-operative parameters,
The system is
UIL, LIL, patient age, pelvic morphology preoperative value, pelvic tilt preoperative value, lumbar lordotic preoperative value, thoracic kyphosis preoperative value, or sagittal plane by analyzing one or more medical images Determining one or more preoperative variables associated with the patient's spine, including at least one of the vertical axis preoperative values; and one or more procedures based at least in part on the application of the predictive model. Generating predictive values for post-variables, wherein the predictive model is
Accessing a data set of an electronic database containing data collected from one or more previous patients and spinal surgery strategies adopted by said one or more previous patients;
Classifying the dataset into one or more categories based on spinal surgery area knowledge;
Normalizing the data into a first subcategory;
Selecting a model algorithm for the data in the first subcategory;
Inputting a first set of input values from the first subcategory into the model algorithm to train a predictive model based on a first set of output values from the first subcategory;
Input a second set of input values from the second subcategory into a trained predictive model and compare the results produced by the trained predictive model with a second set of output values from the second subcategory. Saving the trained predictive model for implementation;
Generate a predicted value, generated by
Further generating predictive values for one or more post-operative parameters by
12. The system of claim 11, wherein the post-operative parameters include one or more of pelvic tilt, lumbar lordosis, thoracic kyphosis, or sagittal vertical axis. 9. The medical image of the one or more vertebrae comprises one or more of a sagittal plane image, an anterior plane image, a flexion X-ray image, a stretched X-ray image, or an MRI image. The system described in. The one or more medical images include one or more two-dimensional X-ray images, the system calibrates the one or more two-dimensional X-ray images, and the one or more two-dimensional X-ray images The system according to claim 8, further comprising generating a three-dimensional composite image based on the X-ray image. A system for developing one or more patient-specific spinal implants, comprising:
One or more computer-readable storage devices configured to store computer-executable instructions; and instructions executable by the computer-readable storage devices in communication with the one or more computer-readable storage devices. To the system,
Accessing one or more medical images of the patient's spine;
Determining the height of each of the one or more intervertebral discs of the spinal segment of interest from the one or more medical images;
Disc height as a percentage of the total disc height of the spinal segment of interest and/or disc as a percentage of the total disc angulation of the spinal segment of interest for each of one or more intervertebral discs of the spinal segment of interest Determining horn formation;
The determined disc height percentage and/or the determined disc angulation percentage of each of the one or more intervertebral discs of the target spinal segment is determined by a predetermined disc height of the intervertebral disc of one or more corresponding asymptomatic populations. Analysis by comparing with a predetermined percentage and/or a predetermined percentage of disc angulation, said predetermined disc height percentage of said one or more corresponding subclinical populations and/or said predetermined disc height. Analyzing, the disc angulation percentage is updated regularly and/or continuously;
One or more correspondences of the determined disc height percentage and/or the determined disc angulation percentage of each of the one or more intervertebral discs of the spinal segment of interest based on the one or more medical images The spinal segment of interest of one or more cages based at least in part on a comparison with the predetermined disc height percentage and/or the predetermined disc angulation percentage of the discs of the asymptomatic population Simulating implantation in one or more intervertebral spaces within;
A posterior height and anterior height and/or angle of each of the one or more patient-specific cages for the spinal segment of interest based at least in part on a simulation of implantation of the one or more cages. Determining one or more dimensions including formation;
Of the existing range based at least in part on the cage manufacturing or data selection instructions generated, based at least in part on the determined one or more dimensions of each of the one or more patient-specific cages. Cage making or data for use by a cage making or selecting device for making or selecting one or more patient-specific cages for said one or more intervertebral space made or selected from a cage Generating a selection instruction,
Determining a screw insertion axis projection length on the sagittal plane and a vertebral body width for each vertebra in the spinal segment of interest from the one or more medical images;
Implantation for each vertebra in the spinal segment of interest based on a given spinal anatomical data, literature, or surgeon preference, at least in part, relative to an end plate configured to attach the screw. Assumed or prescribed angulation of the screw, assumed or prescribed angle between vertebral axis and pedicle axis on cross-section, assumed or prescribed ratio between screw length and screw insertion axis length, and vertebral body width and vertebra Determining an assumed or predetermined ratio between pedicle width;
The determined screw insertion axis projected length on the sagittal plane, the determined vertebral body width, the assumed or predetermined angle between the vertebral axis and the pedicle axis on the cross section, and the screw length and screw One or more of one or more patient-specific screws for insertion into each vertebra in the spinal segment of interest based at least in part on an assumed or predetermined ratio between the length of the insertion axis; Producing the desired length;
Based on, at least in part, a determined screw insertion axis projection length on the sagittal plane, the determined vertebral body width, and an assumed or predetermined ratio between the vertebral body width and the pedicle width. Creating one or more desired diameters of the one or more patient-specific screws for insertion into each vertebra in a spinal segment of interest; and the determined one or more desired lengths And screw manufacturing or data for use by a screw manufacturing or selecting device for making or selecting said one or more patient-specific screws from an existing range of screws based at least in part on a desired diameter. Generating a selection command,
A system comprising one or more hardware computer processors configured to perform. At least one of the one or more patient-specific screws includes one or more sensors, the one or more sensors configured to obtain intraoperative follow-up data, wherein the intra-operative follow-up data is The system of claim 0, including substantially real-time orientation and position data of a portion of the patient's spine, the intraoperative tracking data being configured to assist surgery. The system is
Based on the one or more medical images,
Identifying one or more reference points along the spinal segment of interest; and identifying one or more portions of the one or more medical images around the identified one or more reference points. Rotating to obtain the desired surgical output curvature of the patient's spine,
Simulating the implantation of a spinal rod into the spinal segment of interest further comprising:
Determining one or more dimensions, including diameter and curvature, of the patient-specific spinal rod for the spinal segment of interest based at least in part on a simulation of implantation of the spinal rod;
Use in a spinal rod manufacturing or selection device for making or selecting a patient-specific spinal rod for a spinal segment of interest based at least in part on the determined dimensions of the one or more patient-specific spinal rods. Generating spinal rod manufacturing or data selection instructions for,
Is further implemented,
The patient-specific spinal rod for the spinal segment of interest is made or selected from an existing range of spinal rods based at least in part on the generated spinal rod manufacturing or data selection instructions. system. The desired surgical output curvature of the spine is determined based at least in part on one or more predicted post-operative parameters, the system comprising:
UIL, LIL, patient age, pelvic morphology preoperative value, pelvic tilt preoperative value, lumbar lordotic preoperative value, thoracic kyphosis preoperative value, or sagittal plane by analyzing one or more medical images Determining one or more preoperative variables associated with the patient's spine, including vertical axis preoperative values; and predicting one or more postoperative variables based at least in part on the application of the predictive model Generating a value, the predictive model comprising:
Accessing a data set of an electronic database containing data collected from one or more previous patients and spinal surgery strategies adopted by said one or more previous patients;
Classifying the dataset into one or more categories based on spinal surgery area knowledge;
Normalizing the data into a first subcategory;
Selecting a model algorithm for the data in the first subcategory;
Inputting a first set of input values from the first subcategory into the model algorithm to train a predictive model based on a first set of output values from the first subcategory;
Input a second set of input values from the second subcategory into a trained predictive model and compare the results produced by the trained predictive model with a second set of output values from the second subcategory. Saving the trained predictive model for implementation;
Generate a predicted value, generated by
Further generating predictive values for one or more post-operative parameters by
18. The system of claim 17, wherein the post-operative parameters include one or more of pelvic tilt, lumbar lordosis, thoracic kyphosis, or sagittal vertical axis. The medical image of the one or more vertebrae includes one or more of a sagittal plane radiographic image, a frontal radiographic image, a flexed radiographic image, a stretched radiographic image, or an MRI image. The system described in. The one or more medical images include one or more two-dimensional X-ray images, the system calibrates the one or more two-dimensional X-ray images, and the one or more two-dimensional X-ray images The system of claim 0, further causing generating a three-dimensional composite image based on the x-ray image.